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CONTRIBUTING + Ruff format

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74
CONTRIBUTING.md Normal file
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@ -0,0 +1,74 @@
# Contributing to Kokoro-FastAPI
Always appreciate community involvement in making this project better.
## Development Setup
We use `uv` for managing Python environments and dependencies, and `ruff` for linting and formatting.
1. **Clone the repository:**
```bash
git clone https://github.com/remsky/Kokoro-FastAPI.git
cd Kokoro-FastAPI
```
2. **Install `uv`:**
Follow the instructions on the [official `uv` documentation](https://docs.astral.sh/uv/install/).
3. **Create a virtual environment and install dependencies:**
It's recommended to use a virtual environment. `uv` can create one for you. Install the base dependencies along with the `test` and `cpu` extras (needed for running tests locally).
```bash
# Create and activate a virtual environment (e.g., named .venv)
uv venv
source .venv/bin/activate # On Linux/macOS
# .venv\Scripts\activate # On Windows
# Install dependencies including test requirements
uv pip install -e ".[test,cpu]"
```
*Note: If you have an NVIDIA GPU and want to test GPU-specific features locally, you can install `.[test,gpu]` instead, ensuring you have the correct CUDA toolkit installed.*
*Note: If running via uv locally, you will have to install espeak and handle any pathing issues that arise. The Docker images handle this automatically*
4. **Install `ruff` (if not already installed globally):**
While `ruff` might be included via dependencies, installing it explicitly ensures you have it available.
```bash
uv pip install ruff
```
## Running Tests
Before submitting changes, please ensure all tests pass as this is a automated requirement. The tests are run using `pytest`.
```bash
# Make sure your virtual environment is activated
uv run pytest
```
*Note: The CI workflow runs tests using `uv run pytest api/tests/ --asyncio-mode=auto --cov=api --cov-report=term-missing`. Running `uv run pytest` locally should cover the essential checks.*
## Code Formatting and Linting
We use `ruff` to maintain code quality and consistency. Please format and lint your code before committing.
1. **Format the code:**
```bash
# Make sure your virtual environment is activated
ruff format .
```
2. **Lint the code (and apply automatic fixes):**
```bash
# Make sure your virtual environment is activated
ruff check . --fix
```
Review any changes made by `--fix` and address any remaining linting errors manually.
## Submitting Changes
0. Clone the repo
1. Create a new branch for your feature or bug fix.
2. Make your changes, following setup, testing, and formatting guidelines above.
3. Please try to keep your changes inline with the current design, and modular. Large-scale changes will take longer to review and integrate, and have less chance of being approved outright.
4. Push your branch to your fork.
5. Open a Pull Request against the `master` branch of the main repository.
Thank you for contributing!

28
api/src/core/config.py
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@ -14,9 +14,13 @@ class Settings(BaseSettings):
output_dir: str = "output" output_dir: str = "output"
output_dir_size_limit_mb: float = 500.0 # Maximum size of output directory in MB output_dir_size_limit_mb: float = 500.0 # Maximum size of output directory in MB
default_voice: str = "af_heart" default_voice: str = "af_heart"
default_voice_code: str | None = None # If set, overrides the first letter of voice name, though api call param still takes precedence default_voice_code: str | None = (
None # If set, overrides the first letter of voice name, though api call param still takes precedence
)
use_gpu: bool = True # Whether to use GPU acceleration if available use_gpu: bool = True # Whether to use GPU acceleration if available
device_type: str | None = None # Will be auto-detected if None, can be "cuda", "mps", or "cpu" device_type: str | None = (
None # Will be auto-detected if None, can be "cuda", "mps", or "cpu"
)
allow_local_voice_saving: bool = ( allow_local_voice_saving: bool = (
False # Whether to allow saving combined voices locally False # Whether to allow saving combined voices locally
) )
@ -31,12 +35,21 @@ class Settings(BaseSettings):
target_min_tokens: int = 175 # Target minimum tokens per chunk target_min_tokens: int = 175 # Target minimum tokens per chunk
target_max_tokens: int = 250 # Target maximum tokens per chunk target_max_tokens: int = 250 # Target maximum tokens per chunk
absolute_max_tokens: int = 450 # Absolute maximum tokens per chunk absolute_max_tokens: int = 450 # Absolute maximum tokens per chunk
advanced_text_normalization: bool = True # Preproesses the text before misiki advanced_text_normalization: bool = True # Preproesses the text before misiki
voice_weight_normalization: bool = True # Normalize the voice weights so they add up to 1 voice_weight_normalization: bool = (
True # Normalize the voice weights so they add up to 1
)
gap_trim_ms: int = 1 # Base amount to trim from streaming chunk ends in milliseconds gap_trim_ms: int = (
dynamic_gap_trim_padding_ms: int = 410 # Padding to add to dynamic gap trim 1 # Base amount to trim from streaming chunk ends in milliseconds
dynamic_gap_trim_padding_char_multiplier: dict[str,float] = {".":1,"!":0.9,"?":1,",":0.8} )
dynamic_gap_trim_padding_ms: int = 410 # Padding to add to dynamic gap trim
dynamic_gap_trim_padding_char_multiplier: dict[str, float] = {
".": 1,
"!": 0.9,
"?": 1,
",": 0.8,
}
# Web Player Settings # Web Player Settings
enable_web_player: bool = True # Whether to serve the web player UI enable_web_player: bool = True # Whether to serve the web player UI
@ -69,5 +82,4 @@ class Settings(BaseSettings):
return "cpu" return "cpu"
settings = Settings() settings = Settings()

28
api/src/inference/base.py
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@ -10,26 +10,32 @@ import torch
class AudioChunk: class AudioChunk:
"""Class for audio chunks returned by model backends""" """Class for audio chunks returned by model backends"""
def __init__(self, def __init__(
audio: np.ndarray, self,
word_timestamps: Optional[List]=[], audio: np.ndarray,
output: Optional[Union[bytes,np.ndarray]]=b"" word_timestamps: Optional[List] = [],
): output: Optional[Union[bytes, np.ndarray]] = b"",
self.audio=audio ):
self.word_timestamps=word_timestamps self.audio = audio
self.output=output self.word_timestamps = word_timestamps
self.output = output
@staticmethod @staticmethod
def combine(audio_chunk_list: List): def combine(audio_chunk_list: List):
output=AudioChunk(audio_chunk_list[0].audio,audio_chunk_list[0].word_timestamps) output = AudioChunk(
audio_chunk_list[0].audio, audio_chunk_list[0].word_timestamps
)
for audio_chunk in audio_chunk_list[1:]: for audio_chunk in audio_chunk_list[1:]:
output.audio=np.concatenate((output.audio,audio_chunk.audio),dtype=np.int16) output.audio = np.concatenate(
(output.audio, audio_chunk.audio), dtype=np.int16
)
if output.word_timestamps is not None: if output.word_timestamps is not None:
output.word_timestamps+=audio_chunk.word_timestamps output.word_timestamps += audio_chunk.word_timestamps
return output return output
class ModelBackend(ABC): class ModelBackend(ABC):
"""Abstract base class for model inference backend.""" """Abstract base class for model inference backend."""

46
api/src/inference/kokoro_v1.py
View file

@ -51,7 +51,9 @@ class KokoroV1(BaseModelBackend):
self._model = KModel(config=config_path, model=model_path).eval() self._model = KModel(config=config_path, model=model_path).eval()
# For MPS, manually move ISTFT layers to CPU while keeping rest on MPS # For MPS, manually move ISTFT layers to CPU while keeping rest on MPS
if self._device == "mps": if self._device == "mps":
logger.info("Moving model to MPS device with CPU fallback for unsupported operations") logger.info(
"Moving model to MPS device with CPU fallback for unsupported operations"
)
self._model = self._model.to(torch.device("mps")) self._model = self._model.to(torch.device("mps"))
elif self._device == "cuda": elif self._device == "cuda":
self._model = self._model.cuda() self._model = self._model.cuda()
@ -146,11 +148,11 @@ class KokoroV1(BaseModelBackend):
voice_path = temp_path voice_path = temp_path
# Use provided lang_code, settings voice code override, or first letter of voice name # Use provided lang_code, settings voice code override, or first letter of voice name
if lang_code: # api is given priority if lang_code: # api is given priority
pipeline_lang_code = lang_code pipeline_lang_code = lang_code
elif settings.default_voice_code: # settings is next priority elif settings.default_voice_code: # settings is next priority
pipeline_lang_code = settings.default_voice_code pipeline_lang_code = settings.default_voice_code
else: # voice name is default/fallback else: # voice name is default/fallback
pipeline_lang_code = voice_name[0].lower() pipeline_lang_code = voice_name[0].lower()
pipeline = self._get_pipeline(pipeline_lang_code) pipeline = self._get_pipeline(pipeline_lang_code)
@ -245,7 +247,15 @@ class KokoroV1(BaseModelBackend):
voice_path = temp_path voice_path = temp_path
# Use provided lang_code, settings voice code override, or first letter of voice name # Use provided lang_code, settings voice code override, or first letter of voice name
pipeline_lang_code = lang_code if lang_code else (settings.default_voice_code if settings.default_voice_code else voice_name[0].lower()) pipeline_lang_code = (
lang_code
if lang_code
else (
settings.default_voice_code
if settings.default_voice_code
else voice_name[0].lower()
)
)
pipeline = self._get_pipeline(pipeline_lang_code) pipeline = self._get_pipeline(pipeline_lang_code)
logger.debug( logger.debug(
@ -256,16 +266,19 @@ class KokoroV1(BaseModelBackend):
): ):
if result.audio is not None: if result.audio is not None:
logger.debug(f"Got audio chunk with shape: {result.audio.shape}") logger.debug(f"Got audio chunk with shape: {result.audio.shape}")
word_timestamps=None word_timestamps = None
if return_timestamps and hasattr(result, "tokens") and result.tokens: if (
word_timestamps=[] return_timestamps
current_offset=0.0 and hasattr(result, "tokens")
and result.tokens
):
word_timestamps = []
current_offset = 0.0
logger.debug( logger.debug(
f"Processing chunk timestamps with {len(result.tokens)} tokens" f"Processing chunk timestamps with {len(result.tokens)} tokens"
) )
if result.pred_dur is not None: if result.pred_dur is not None:
try: try:
# Add timestamps with offset # Add timestamps with offset
for token in result.tokens: for token in result.tokens:
if not all( if not all(
@ -286,7 +299,7 @@ class KokoroV1(BaseModelBackend):
WordTimestamp( WordTimestamp(
word=str(token.text).strip(), word=str(token.text).strip(),
start_time=start_time, start_time=start_time,
end_time=end_time end_time=end_time,
) )
) )
logger.debug( logger.debug(
@ -298,8 +311,9 @@ class KokoroV1(BaseModelBackend):
f"Failed to process timestamps for chunk: {e}" f"Failed to process timestamps for chunk: {e}"
) )
yield AudioChunk(
yield AudioChunk(result.audio.numpy(),word_timestamps=word_timestamps) result.audio.numpy(), word_timestamps=word_timestamps
)
else: else:
logger.warning("No audio in chunk") logger.warning("No audio in chunk")
@ -330,7 +344,7 @@ class KokoroV1(BaseModelBackend):
torch.cuda.synchronize() torch.cuda.synchronize()
elif self._device == "mps": elif self._device == "mps":
# Empty cache if available (future-proofing) # Empty cache if available (future-proofing)
if hasattr(torch.mps, 'empty_cache'): if hasattr(torch.mps, "empty_cache"):
torch.mps.empty_cache() torch.mps.empty_cache()
def unload(self) -> None: def unload(self) -> None:

12
api/src/routers/debug.py
View file

@ -115,12 +115,12 @@ async def get_system_info():
# GPU Info if available # GPU Info if available
gpu_info = None gpu_info = None
if torch.backends.mps.is_available(): if torch.backends.mps.is_available():
gpu_info = { gpu_info = {
"type": "MPS", "type": "MPS",
"available": True, "available": True,
"device": "Apple Silicon", "device": "Apple Silicon",
"backend": "Metal" "backend": "Metal",
} }
elif GPU_AVAILABLE: elif GPU_AVAILABLE:
try: try:
gpus = GPUtil.getGPUs() gpus = GPUtil.getGPUs()

65
api/src/routers/development.py
View file

@ -156,6 +156,7 @@ async def generate_from_phonemes(
}, },
) )
@router.post("/dev/captioned_speech") @router.post("/dev/captioned_speech")
async def create_captioned_speech( async def create_captioned_speech(
request: CaptionedSpeechRequest, request: CaptionedSpeechRequest,
@ -184,7 +185,9 @@ async def create_captioned_speech(
# Check if streaming is requested (default for OpenAI client) # Check if streaming is requested (default for OpenAI client)
if request.stream: if request.stream:
# Create generator but don't start it yet # Create generator but don't start it yet
generator = stream_audio_chunks(tts_service, request, client_request, writer) generator = stream_audio_chunks(
tts_service, request, client_request, writer
)
# If download link requested, wrap generator with temp file writer # If download link requested, wrap generator with temp file writer
if request.return_download_link: if request.return_download_link:
@ -211,20 +214,31 @@ async def create_captioned_speech(
# Write chunks to temp file and stream # Write chunks to temp file and stream
async for chunk_data in generator: async for chunk_data in generator:
# The timestamp acumulator is only used when word level time stamps are generated but no audio is returned. # The timestamp acumulator is only used when word level time stamps are generated but no audio is returned.
timestamp_acumulator=[] timestamp_acumulator = []
if chunk_data.output: # Skip empty chunks if chunk_data.output: # Skip empty chunks
await temp_writer.write(chunk_data.output) await temp_writer.write(chunk_data.output)
base64_chunk= base64.b64encode(chunk_data.output).decode("utf-8") base64_chunk = base64.b64encode(
chunk_data.output
).decode("utf-8")
# Add any chunks that may be in the acumulator into the return word_timestamps # Add any chunks that may be in the acumulator into the return word_timestamps
chunk_data.word_timestamps=timestamp_acumulator + chunk_data.word_timestamps chunk_data.word_timestamps = (
timestamp_acumulator=[] timestamp_acumulator + chunk_data.word_timestamps
)
timestamp_acumulator = []
yield CaptionedSpeechResponse(audio=base64_chunk,audio_format=content_type,timestamps=chunk_data.word_timestamps) yield CaptionedSpeechResponse(
audio=base64_chunk,
audio_format=content_type,
timestamps=chunk_data.word_timestamps,
)
else: else:
if chunk_data.word_timestamps is not None and len(chunk_data.word_timestamps) > 0: if (
timestamp_acumulator+=chunk_data.word_timestamps chunk_data.word_timestamps is not None
and len(chunk_data.word_timestamps) > 0
):
timestamp_acumulator += chunk_data.word_timestamps
# Finalize the temp file # Finalize the temp file
await temp_writer.finalize() await temp_writer.finalize()
@ -246,25 +260,36 @@ async def create_captioned_speech(
async def single_output(): async def single_output():
try: try:
# The timestamp acumulator is only used when word level time stamps are generated but no audio is returned. # The timestamp acumulator is only used when word level time stamps are generated but no audio is returned.
timestamp_acumulator=[] timestamp_acumulator = []
# Stream chunks # Stream chunks
async for chunk_data in generator: async for chunk_data in generator:
if chunk_data.output: # Skip empty chunks if chunk_data.output: # Skip empty chunks
# Encode the chunk bytes into base 64 # Encode the chunk bytes into base 64
base64_chunk= base64.b64encode(chunk_data.output).decode("utf-8") base64_chunk = base64.b64encode(chunk_data.output).decode(
"utf-8"
)
# Add any chunks that may be in the acumulator into the return word_timestamps # Add any chunks that may be in the acumulator into the return word_timestamps
if chunk_data.word_timestamps != None: if chunk_data.word_timestamps != None:
chunk_data.word_timestamps = timestamp_acumulator + chunk_data.word_timestamps chunk_data.word_timestamps = (
timestamp_acumulator + chunk_data.word_timestamps
)
else: else:
chunk_data.word_timestamps = [] chunk_data.word_timestamps = []
timestamp_acumulator=[] timestamp_acumulator = []
yield CaptionedSpeechResponse(audio=base64_chunk,audio_format=content_type,timestamps=chunk_data.word_timestamps) yield CaptionedSpeechResponse(
audio=base64_chunk,
audio_format=content_type,
timestamps=chunk_data.word_timestamps,
)
else: else:
if chunk_data.word_timestamps is not None and len(chunk_data.word_timestamps) > 0: if (
timestamp_acumulator+=chunk_data.word_timestamps chunk_data.word_timestamps is not None
and len(chunk_data.word_timestamps) > 0
):
timestamp_acumulator += chunk_data.word_timestamps
except Exception as e: except Exception as e:
logger.error(f"Error in single output streaming: {e}") logger.error(f"Error in single output streaming: {e}")
@ -309,11 +334,15 @@ async def create_captioned_speech(
writer, writer,
is_last_chunk=True, is_last_chunk=True,
) )
output=audio_data.output + final.output output = audio_data.output + final.output
base64_output= base64.b64encode(output).decode("utf-8") base64_output = base64.b64encode(output).decode("utf-8")
content=CaptionedSpeechResponse(audio=base64_output,audio_format=content_type,timestamps=audio_data.word_timestamps).model_dump() content = CaptionedSpeechResponse(
audio=base64_output,
audio_format=content_type,
timestamps=audio_data.word_timestamps,
).model_dump()
writer.close() writer.close()

103
api/src/routers/openai_compatible.py
View file

@ -80,7 +80,9 @@ def get_model_name(model: str) -> str:
return base_name + ".pth" return base_name + ".pth"
async def process_and_validate_voices(voice_input: Union[str, List[str]], tts_service: TTSService) -> str: async def process_and_validate_voices(
voice_input: Union[str, List[str]], tts_service: TTSService
) -> str:
"""Process voice input, handling both string and list formats """Process voice input, handling both string and list formats
Returns: Returns:
@ -107,22 +109,35 @@ async def process_and_validate_voices(voice_input: Union[str, List[str]], tts_se
mapped_voice = list(map(str.strip, mapped_voice)) mapped_voice = list(map(str.strip, mapped_voice))
if len(mapped_voice) > 2: if len(mapped_voice) > 2:
raise ValueError(f"Voice '{voices[voice_index]}' contains too many weight items") raise ValueError(
f"Voice '{voices[voice_index]}' contains too many weight items"
)
if mapped_voice.count(")") > 1: if mapped_voice.count(")") > 1:
raise ValueError(f"Voice '{voices[voice_index]}' contains too many weight items") raise ValueError(
f"Voice '{voices[voice_index]}' contains too many weight items"
)
mapped_voice[0] = _openai_mappings["voices"].get(mapped_voice[0], mapped_voice[0]) mapped_voice[0] = _openai_mappings["voices"].get(
mapped_voice[0], mapped_voice[0]
)
if mapped_voice[0] not in available_voices: if mapped_voice[0] not in available_voices:
raise ValueError(f"Voice '{mapped_voice[0]}' not found. Available voices: {', '.join(sorted(available_voices))}") raise ValueError(
f"Voice '{mapped_voice[0]}' not found. Available voices: {', '.join(sorted(available_voices))}"
)
voices[voice_index] = "(".join(mapped_voice) voices[voice_index] = "(".join(mapped_voice)
return "".join(voices) return "".join(voices)
async def stream_audio_chunks(tts_service: TTSService, request: Union[OpenAISpeechRequest, CaptionedSpeechRequest], client_request: Request, writer: StreamingAudioWriter) -> AsyncGenerator[AudioChunk, None]: async def stream_audio_chunks(
tts_service: TTSService,
request: Union[OpenAISpeechRequest, CaptionedSpeechRequest],
client_request: Request,
writer: StreamingAudioWriter,
) -> AsyncGenerator[AudioChunk, None]:
"""Stream audio chunks as they're generated with client disconnect handling""" """Stream audio chunks as they're generated with client disconnect handling"""
voice_name = await process_and_validate_voices(request.voice, tts_service) voice_name = await process_and_validate_voices(request.voice, tts_service)
unique_properties = {"return_timestamps": False} unique_properties = {"return_timestamps": False}
@ -193,7 +208,9 @@ async def create_speech(
# Check if streaming is requested (default for OpenAI client) # Check if streaming is requested (default for OpenAI client)
if request.stream: if request.stream:
# Create generator but don't start it yet # Create generator but don't start it yet
generator = stream_audio_chunks(tts_service, request, client_request, writer) generator = stream_audio_chunks(
tts_service, request, client_request, writer
)
# If download link requested, wrap generator with temp file writer # If download link requested, wrap generator with temp file writer
if request.return_download_link: if request.return_download_link:
@ -245,7 +262,9 @@ async def create_speech(
writer.close() writer.close()
# Stream with temp file writing # Stream with temp file writing
return StreamingResponse(dual_output(), media_type=content_type, headers=headers) return StreamingResponse(
dual_output(), media_type=content_type, headers=headers
)
async def single_output(): async def single_output():
try: try:
@ -285,7 +304,13 @@ async def create_speech(
lang_code=request.lang_code, lang_code=request.lang_code,
) )
audio_data = await AudioService.convert_audio(audio_data, request.response_format, writer, is_last_chunk=False, trim_audio=False) audio_data = await AudioService.convert_audio(
audio_data,
request.response_format,
writer,
is_last_chunk=False,
trim_audio=False,
)
# Convert to requested format with proper finalization # Convert to requested format with proper finalization
final = await AudioService.convert_audio( final = await AudioService.convert_audio(
@ -384,7 +409,6 @@ async def create_speech(
) )
@router.get("/download/{filename}") @router.get("/download/{filename}")
async def download_audio_file(filename: str): async def download_audio_file(filename: str):
"""Download a generated audio file from temp storage""" """Download a generated audio file from temp storage"""
@ -392,7 +416,9 @@ async def download_audio_file(filename: str):
from ..core.paths import _find_file, get_content_type from ..core.paths import _find_file, get_content_type
# Search for file in temp directory # Search for file in temp directory
file_path = await _find_file(filename=filename, search_paths=[settings.temp_file_dir]) file_path = await _find_file(
filename=filename, search_paths=[settings.temp_file_dir]
)
# Get content type from path helper # Get content type from path helper
content_type = await get_content_type(file_path) content_type = await get_content_type(file_path)
@ -425,9 +451,24 @@ async def list_models():
try: try:
# Create standard model list # Create standard model list
models = [ models = [
{"id": "tts-1", "object": "model", "created": 1686935002, "owned_by": "kokoro"}, {
{"id": "tts-1-hd", "object": "model", "created": 1686935002, "owned_by": "kokoro"}, "id": "tts-1",
{"id": "kokoro", "object": "model", "created": 1686935002, "owned_by": "kokoro"}, "object": "model",
"created": 1686935002,
"owned_by": "kokoro",
},
{
"id": "tts-1-hd",
"object": "model",
"created": 1686935002,
"owned_by": "kokoro",
},
{
"id": "kokoro",
"object": "model",
"created": 1686935002,
"owned_by": "kokoro",
},
] ]
return {"object": "list", "data": models} return {"object": "list", "data": models}
@ -449,14 +490,36 @@ async def retrieve_model(model: str):
try: try:
# Define available models # Define available models
models = { models = {
"tts-1": {"id": "tts-1", "object": "model", "created": 1686935002, "owned_by": "kokoro"}, "tts-1": {
"tts-1-hd": {"id": "tts-1-hd", "object": "model", "created": 1686935002, "owned_by": "kokoro"}, "id": "tts-1",
"kokoro": {"id": "kokoro", "object": "model", "created": 1686935002, "owned_by": "kokoro"}, "object": "model",
"created": 1686935002,
"owned_by": "kokoro",
},
"tts-1-hd": {
"id": "tts-1-hd",
"object": "model",
"created": 1686935002,
"owned_by": "kokoro",
},
"kokoro": {
"id": "kokoro",
"object": "model",
"created": 1686935002,
"owned_by": "kokoro",
},
} }
# Check if requested model exists # Check if requested model exists
if model not in models: if model not in models:
raise HTTPException(status_code=404, detail={"error": "model_not_found", "message": f"Model '{model}' not found", "type": "invalid_request_error"}) raise HTTPException(
status_code=404,
detail={
"error": "model_not_found",
"message": f"Model '{model}' not found",
"type": "invalid_request_error",
},
)
# Return the specific model # Return the specific model
return models[model] return models[model]
@ -541,7 +604,9 @@ async def combine_voices(request: Union[str, List[str]]):
available_voices = await tts_service.list_voices() available_voices = await tts_service.list_voices()
for voice in voices: for voice in voices:
if voice not in available_voices: if voice not in available_voices:
raise ValueError(f"Base voice '{voice}' not found. Available voices: {', '.join(sorted(available_voices))}") raise ValueError(
f"Base voice '{voice}' not found. Available voices: {', '.join(sorted(available_voices))}"
)
# Combine voices # Combine voices
combined_tensor = await tts_service.combine_voices(voices=voices) combined_tensor = await tts_service.combine_voices(voices=voices)

96
api/src/services/audio.py
View file

@ -25,9 +25,16 @@ class AudioNormalizer:
self.chunk_trim_ms = settings.gap_trim_ms self.chunk_trim_ms = settings.gap_trim_ms
self.sample_rate = 24000 # Sample rate of the audio self.sample_rate = 24000 # Sample rate of the audio
self.samples_to_trim = int(self.chunk_trim_ms * self.sample_rate / 1000) self.samples_to_trim = int(self.chunk_trim_ms * self.sample_rate / 1000)
self.samples_to_pad_start= int(50 * self.sample_rate / 1000) self.samples_to_pad_start = int(50 * self.sample_rate / 1000)
def find_first_last_non_silent(self,audio_data: np.ndarray, chunk_text: str, speed: float, silence_threshold_db: int = -45, is_last_chunk: bool = False) -> tuple[int, int]: def find_first_last_non_silent(
self,
audio_data: np.ndarray,
chunk_text: str,
speed: float,
silence_threshold_db: int = -45,
is_last_chunk: bool = False,
) -> tuple[int, int]:
"""Finds the indices of the first and last non-silent samples in audio data. """Finds the indices of the first and last non-silent samples in audio data.
Args: Args:
@ -41,37 +48,55 @@ class AudioNormalizer:
A tuple with the start of the non silent portion and with the end of the non silent portion A tuple with the start of the non silent portion and with the end of the non silent portion
""" """
pad_multiplier=1 pad_multiplier = 1
split_character=chunk_text.strip() split_character = chunk_text.strip()
if len(split_character) > 0: if len(split_character) > 0:
split_character=split_character[-1] split_character = split_character[-1]
if split_character in settings.dynamic_gap_trim_padding_char_multiplier: if split_character in settings.dynamic_gap_trim_padding_char_multiplier:
pad_multiplier=settings.dynamic_gap_trim_padding_char_multiplier[split_character] pad_multiplier = settings.dynamic_gap_trim_padding_char_multiplier[
split_character
]
if not is_last_chunk: if not is_last_chunk:
samples_to_pad_end= max(int((settings.dynamic_gap_trim_padding_ms * self.sample_rate * pad_multiplier) / 1000) - self.samples_to_pad_start, 0) samples_to_pad_end = max(
int(
(
settings.dynamic_gap_trim_padding_ms
* self.sample_rate
* pad_multiplier
)
/ 1000
)
- self.samples_to_pad_start,
0,
)
else: else:
samples_to_pad_end=self.samples_to_pad_start samples_to_pad_end = self.samples_to_pad_start
# Convert dBFS threshold to amplitude # Convert dBFS threshold to amplitude
amplitude_threshold = np.iinfo(audio_data.dtype).max * (10 ** (silence_threshold_db / 20)) amplitude_threshold = np.iinfo(audio_data.dtype).max * (
10 ** (silence_threshold_db / 20)
)
# Find the first samples above the silence threshold at the start and end of the audio # Find the first samples above the silence threshold at the start and end of the audio
non_silent_index_start, non_silent_index_end = None,None non_silent_index_start, non_silent_index_end = None, None
for X in range(0,len(audio_data)): for X in range(0, len(audio_data)):
if audio_data[X] > amplitude_threshold: if audio_data[X] > amplitude_threshold:
non_silent_index_start=X non_silent_index_start = X
break break
for X in range(len(audio_data) - 1, -1, -1): for X in range(len(audio_data) - 1, -1, -1):
if audio_data[X] > amplitude_threshold: if audio_data[X] > amplitude_threshold:
non_silent_index_end=X non_silent_index_end = X
break break
# Handle the case where the entire audio is silent # Handle the case where the entire audio is silent
if non_silent_index_start == None or non_silent_index_end == None: if non_silent_index_start == None or non_silent_index_end == None:
return 0, len(audio_data) return 0, len(audio_data)
return max(non_silent_index_start - self.samples_to_pad_start,0), min(non_silent_index_end + math.ceil(samples_to_pad_end / speed),len(audio_data)) return max(non_silent_index_start - self.samples_to_pad_start, 0), min(
non_silent_index_end + math.ceil(samples_to_pad_end / speed),
len(audio_data),
)
def normalize(self, audio_data: np.ndarray) -> np.ndarray: def normalize(self, audio_data: np.ndarray) -> np.ndarray:
"""Convert audio data to int16 range """Convert audio data to int16 range
@ -86,6 +111,7 @@ class AudioNormalizer:
return np.clip(audio_data * 32767, -32768, 32767).astype(np.int16) return np.clip(audio_data * 32767, -32768, 32767).astype(np.int16)
return audio_data return audio_data
class AudioService: class AudioService:
"""Service for audio format conversions with streaming support""" """Service for audio format conversions with streaming support"""
@ -148,24 +174,24 @@ class AudioService:
audio_chunk.audio = normalizer.normalize(audio_chunk.audio) audio_chunk.audio = normalizer.normalize(audio_chunk.audio)
if trim_audio == True: if trim_audio == True:
audio_chunk = AudioService.trim_audio(audio_chunk,chunk_text,speed,is_last_chunk,normalizer) audio_chunk = AudioService.trim_audio(
audio_chunk, chunk_text, speed, is_last_chunk, normalizer
)
# Write audio data first # Write audio data first
if len(audio_chunk.audio) > 0: if len(audio_chunk.audio) > 0:
chunk_data = writer.write_chunk(audio_chunk.audio) chunk_data = writer.write_chunk(audio_chunk.audio)
# Then finalize if this is the last chunk # Then finalize if this is the last chunk
if is_last_chunk: if is_last_chunk:
final_data = writer.write_chunk(finalize=True) final_data = writer.write_chunk(finalize=True)
if final_data: if final_data:
audio_chunk.output=final_data audio_chunk.output = final_data
return audio_chunk return audio_chunk
if chunk_data: if chunk_data:
audio_chunk.output=chunk_data audio_chunk.output = chunk_data
return audio_chunk return audio_chunk
except Exception as e: except Exception as e:
@ -173,8 +199,15 @@ class AudioService:
raise ValueError( raise ValueError(
f"Failed to convert audio stream to {output_format}: {str(e)}" f"Failed to convert audio stream to {output_format}: {str(e)}"
) )
@staticmethod @staticmethod
def trim_audio(audio_chunk: AudioChunk, chunk_text: str = "", speed: float = 1, is_last_chunk: bool = False, normalizer: AudioNormalizer = None) -> AudioChunk: def trim_audio(
audio_chunk: AudioChunk,
chunk_text: str = "",
speed: float = 1,
is_last_chunk: bool = False,
normalizer: AudioNormalizer = None,
) -> AudioChunk:
"""Trim silence from start and end """Trim silence from start and end
Args: Args:
@ -190,23 +223,26 @@ class AudioService:
if normalizer is None: if normalizer is None:
normalizer = AudioNormalizer() normalizer = AudioNormalizer()
audio_chunk.audio=normalizer.normalize(audio_chunk.audio) audio_chunk.audio = normalizer.normalize(audio_chunk.audio)
trimed_samples=0 trimed_samples = 0
# Trim start and end if enough samples # Trim start and end if enough samples
if len(audio_chunk.audio) > (2 * normalizer.samples_to_trim): if len(audio_chunk.audio) > (2 * normalizer.samples_to_trim):
audio_chunk.audio = audio_chunk.audio[normalizer.samples_to_trim : -normalizer.samples_to_trim] audio_chunk.audio = audio_chunk.audio[
trimed_samples+=normalizer.samples_to_trim normalizer.samples_to_trim : -normalizer.samples_to_trim
]
trimed_samples += normalizer.samples_to_trim
# Find non silent portion and trim # Find non silent portion and trim
start_index,end_index=normalizer.find_first_last_non_silent(audio_chunk.audio,chunk_text,speed,is_last_chunk=is_last_chunk) start_index, end_index = normalizer.find_first_last_non_silent(
audio_chunk.audio, chunk_text, speed, is_last_chunk=is_last_chunk
)
audio_chunk.audio=audio_chunk.audio[start_index:end_index] audio_chunk.audio = audio_chunk.audio[start_index:end_index]
trimed_samples+=start_index trimed_samples += start_index
if audio_chunk.word_timestamps is not None: if audio_chunk.word_timestamps is not None:
for timestamp in audio_chunk.word_timestamps: for timestamp in audio_chunk.word_timestamps:
timestamp.start_time-=trimed_samples / 24000 timestamp.start_time -= trimed_samples / 24000
timestamp.end_time-=trimed_samples / 24000 timestamp.end_time -= trimed_samples / 24000
return audio_chunk return audio_chunk

36
api/src/services/streaming_audio_writer.py
View file

@ -19,15 +19,29 @@ class StreamingAudioWriter:
self.sample_rate = sample_rate self.sample_rate = sample_rate
self.channels = channels self.channels = channels
self.bytes_written = 0 self.bytes_written = 0
self.pts=0 self.pts = 0
codec_map = {"wav":"pcm_s16le","mp3":"mp3","opus":"libopus","flac":"flac", "aac":"aac"} codec_map = {
"wav": "pcm_s16le",
"mp3": "mp3",
"opus": "libopus",
"flac": "flac",
"aac": "aac",
}
# Format-specific setup # Format-specific setup
if self.format in ["wav","flac","mp3","pcm","aac","opus"]: if self.format in ["wav", "flac", "mp3", "pcm", "aac", "opus"]:
if self.format != "pcm": if self.format != "pcm":
self.output_buffer = BytesIO() self.output_buffer = BytesIO()
self.container = av.open(self.output_buffer, mode="w", format=self.format if self.format != "aac" else "adts") self.container = av.open(
self.stream = self.container.add_stream(codec_map[self.format],sample_rate=self.sample_rate,layout='mono' if self.channels == 1 else 'stereo') self.output_buffer,
mode="w",
format=self.format if self.format != "aac" else "adts",
)
self.stream = self.container.add_stream(
codec_map[self.format],
sample_rate=self.sample_rate,
layout="mono" if self.channels == 1 else "stereo",
)
self.stream.bit_rate = 128000 self.stream.bit_rate = 128000
else: else:
raise ValueError(f"Unsupported format: {format}") raise ValueError(f"Unsupported format: {format}")
@ -55,7 +69,7 @@ class StreamingAudioWriter:
for packet in packets: for packet in packets:
self.container.mux(packet) self.container.mux(packet)
data=self.output_buffer.getvalue() data = self.output_buffer.getvalue()
self.close() self.close()
return data return data
@ -66,9 +80,12 @@ class StreamingAudioWriter:
# Write raw bytes # Write raw bytes
return audio_data.tobytes() return audio_data.tobytes()
else: else:
frame = av.AudioFrame.from_ndarray(audio_data.reshape(1, -1), format='s16', layout='mono' if self.channels == 1 else 'stereo') frame = av.AudioFrame.from_ndarray(
frame.sample_rate=self.sample_rate audio_data.reshape(1, -1),
format="s16",
layout="mono" if self.channels == 1 else "stereo",
)
frame.sample_rate = self.sample_rate
frame.pts = self.pts frame.pts = self.pts
self.pts += frame.samples self.pts += frame.samples
@ -81,4 +98,3 @@ class StreamingAudioWriter:
self.output_buffer.seek(0) self.output_buffer.seek(0)
self.output_buffer.truncate(0) self.output_buffer.truncate(0)
return data return data

170
api/src/services/text_processing/normalizer.py
View file

@ -58,23 +58,77 @@ VALID_TLDS = [
] ]
VALID_UNITS = { VALID_UNITS = {
"m":"meter", "cm":"centimeter", "mm":"millimeter", "km":"kilometer", "in":"inch", "ft":"foot", "yd":"yard", "mi":"mile", # Length "m": "meter",
"g":"gram", "kg":"kilogram", "mg":"miligram", # Mass "cm": "centimeter",
"s":"second", "ms":"milisecond", "min":"minutes", "h":"hour", # Time "mm": "millimeter",
"l":"liter", "ml":"mililiter", "cl":"centiliter", "dl":"deciliter", # Volume "km": "kilometer",
"kph":"kilometer per hour", "mph":"mile per hour","mi/h":"mile per hour", "m/s":"meter per second", "km/h":"kilometer per hour", "mm/s":"milimeter per second","cm/s":"centimeter per second", "ft/s":"feet per second","cm/h":"centimeter per day", # Speed "in": "inch",
"°c":"degree celsius","c":"degree celsius", "°f":"degree fahrenheit","f":"degree fahrenheit", "k":"kelvin", # Temperature "ft": "foot",
"pa":"pascal", "kpa":"kilopascal", "mpa":"megapascal", "atm":"atmosphere", # Pressure "yd": "yard",
"hz":"hertz", "khz":"kilohertz", "mhz":"megahertz", "ghz":"gigahertz", # Frequency "mi": "mile", # Length
"v":"volt", "kv":"kilovolt", "mv":"mergavolt", # Voltage "g": "gram",
"a":"amp", "ma":"megaamp", "ka":"kiloamp", # Current "kg": "kilogram",
"w":"watt", "kw":"kilowatt", "mw":"megawatt", # Power "mg": "miligram", # Mass
"j":"joule", "kj":"kilojoule", "mj":"megajoule", # Energy "s": "second",
"Ω":"ohm", "kΩ":"kiloohm", "mΩ":"megaohm", # Resistance (Ohm) "ms": "milisecond",
"f":"farad", "µf":"microfarad", "nf":"nanofarad", "pf":"picofarad", # Capacitance "min": "minutes",
"b":"bit", "kb":"kilobit", "mb":"megabit", "gb":"gigabit", "tb":"terabit", "pb":"petabit", # Data size "h": "hour", # Time
"kbps":"kilobit per second","mbps":"megabit per second","gbps":"gigabit per second","tbps":"terabit per second", "l": "liter",
"px":"pixel" # CSS units "ml": "mililiter",
"cl": "centiliter",
"dl": "deciliter", # Volume
"kph": "kilometer per hour",
"mph": "mile per hour",
"mi/h": "mile per hour",
"m/s": "meter per second",
"km/h": "kilometer per hour",
"mm/s": "milimeter per second",
"cm/s": "centimeter per second",
"ft/s": "feet per second",
"cm/h": "centimeter per day", # Speed
"°c": "degree celsius",
"c": "degree celsius",
"°f": "degree fahrenheit",
"f": "degree fahrenheit",
"k": "kelvin", # Temperature
"pa": "pascal",
"kpa": "kilopascal",
"mpa": "megapascal",
"atm": "atmosphere", # Pressure
"hz": "hertz",
"khz": "kilohertz",
"mhz": "megahertz",
"ghz": "gigahertz", # Frequency
"v": "volt",
"kv": "kilovolt",
"mv": "mergavolt", # Voltage
"a": "amp",
"ma": "megaamp",
"ka": "kiloamp", # Current
"w": "watt",
"kw": "kilowatt",
"mw": "megawatt", # Power
"j": "joule",
"kj": "kilojoule",
"mj": "megajoule", # Energy
"Ω": "ohm",
"kΩ": "kiloohm",
"mΩ": "megaohm", # Resistance (Ohm)
"f": "farad",
"µf": "microfarad",
"nf": "nanofarad",
"pf": "picofarad", # Capacitance
"b": "bit",
"kb": "kilobit",
"mb": "megabit",
"gb": "gigabit",
"tb": "terabit",
"pb": "petabit", # Data size
"kbps": "kilobit per second",
"mbps": "megabit per second",
"gbps": "gigabit per second",
"tbps": "terabit per second",
"px": "pixel", # CSS units
} }
@ -89,11 +143,19 @@ URL_PATTERN = re.compile(
re.IGNORECASE, re.IGNORECASE,
) )
UNIT_PATTERN = re.compile(r"((?<!\w)([+-]?)(\d{1,3}(,\d{3})*|\d+)(\.\d+)?)\s*(" + "|".join(sorted(list(VALID_UNITS.keys()),reverse=True)) + r"""){1}(?=[^\w\d]{1}|\b)""",re.IGNORECASE) UNIT_PATTERN = re.compile(
r"((?<!\w)([+-]?)(\d{1,3}(,\d{3})*|\d+)(\.\d+)?)\s*("
+ "|".join(sorted(list(VALID_UNITS.keys()), reverse=True))
+ r"""){1}(?=[^\w\d]{1}|\b)""",
re.IGNORECASE,
)
TIME_PATTERN = re.compile(r"([0-9]{2} ?: ?[0-9]{2}( ?: ?[0-9]{2})?)( ?(pm|am)\b)?", re.IGNORECASE) TIME_PATTERN = re.compile(
r"([0-9]{2} ?: ?[0-9]{2}( ?: ?[0-9]{2})?)( ?(pm|am)\b)?", re.IGNORECASE
)
INFLECT_ENGINE = inflect.engine()
INFLECT_ENGINE=inflect.engine()
def split_num(num: re.Match[str]) -> str: def split_num(num: re.Match[str]) -> str:
"""Handle number splitting for various formats""" """Handle number splitting for various formats"""
@ -119,29 +181,32 @@ def split_num(num: re.Match[str]) -> str:
return f"{left} oh {right}{s}" return f"{left} oh {right}{s}"
return f"{left} {right}{s}" return f"{left} {right}{s}"
def handle_units(u: re.Match[str]) -> str: def handle_units(u: re.Match[str]) -> str:
"""Converts units to their full form""" """Converts units to their full form"""
unit_string=u.group(6).strip() unit_string = u.group(6).strip()
unit=unit_string unit = unit_string
if unit_string.lower() in VALID_UNITS: if unit_string.lower() in VALID_UNITS:
unit=VALID_UNITS[unit_string.lower()].split(" ") unit = VALID_UNITS[unit_string.lower()].split(" ")
# Handles the B vs b case # Handles the B vs b case
if unit[0].endswith("bit"): if unit[0].endswith("bit"):
b_case=unit_string[min(1,len(unit_string) - 1)] b_case = unit_string[min(1, len(unit_string) - 1)]
if b_case == "B": if b_case == "B":
unit[0]=unit[0][:-3] + "byte" unit[0] = unit[0][:-3] + "byte"
number=u.group(1).strip() number = u.group(1).strip()
unit[0]=INFLECT_ENGINE.no(unit[0],number) unit[0] = INFLECT_ENGINE.no(unit[0], number)
return " ".join(unit) return " ".join(unit)
def conditional_int(number: float, threshold: float = 0.00001): def conditional_int(number: float, threshold: float = 0.00001):
if abs(round(number) - number) < threshold: if abs(round(number) - number) < threshold:
return int(round(number)) return int(round(number))
return number return number
def handle_money(m: re.Match[str]) -> str: def handle_money(m: re.Match[str]) -> str:
"""Convert money expressions to spoken form""" """Convert money expressions to spoken form"""
@ -167,6 +232,7 @@ def handle_money(m: re.Match[str]) -> str:
return text_number return text_number
def handle_decimal(num: re.Match[str]) -> str: def handle_decimal(num: re.Match[str]) -> str:
"""Convert decimal numbers to spoken form""" """Convert decimal numbers to spoken form"""
a, b = num.group().split(".") a, b = num.group().split(".")
@ -230,34 +296,41 @@ def handle_url(u: re.Match[str]) -> str:
# Clean up extra spaces # Clean up extra spaces
return re.sub(r"\s+", " ", url).strip() return re.sub(r"\s+", " ", url).strip()
def handle_phone_number(p: re.Match[str]) -> str:
p=list(p.groups())
country_code="" def handle_phone_number(p: re.Match[str]) -> str:
p = list(p.groups())
country_code = ""
if p[0] is not None: if p[0] is not None:
p[0]=p[0].replace("+","") p[0] = p[0].replace("+", "")
country_code += INFLECT_ENGINE.number_to_words(p[0]) country_code += INFLECT_ENGINE.number_to_words(p[0])
area_code=INFLECT_ENGINE.number_to_words(p[2].replace("(","").replace(")",""),group=1,comma="") area_code = INFLECT_ENGINE.number_to_words(
p[2].replace("(", "").replace(")", ""), group=1, comma=""
)
telephone_prefix=INFLECT_ENGINE.number_to_words(p[3],group=1,comma="") telephone_prefix = INFLECT_ENGINE.number_to_words(p[3], group=1, comma="")
line_number=INFLECT_ENGINE.number_to_words(p[4],group=1,comma="") line_number = INFLECT_ENGINE.number_to_words(p[4], group=1, comma="")
return ",".join([country_code, area_code, telephone_prefix, line_number])
return ",".join([country_code,area_code,telephone_prefix,line_number])
def handle_time(t: re.Match[str]) -> str: def handle_time(t: re.Match[str]) -> str:
t=t.groups() t = t.groups()
numbers = " ".join([INFLECT_ENGINE.number_to_words(X.strip()) for X in t[0].split(":")]) numbers = " ".join(
[INFLECT_ENGINE.number_to_words(X.strip()) for X in t[0].split(":")]
)
half="" half = ""
if t[2] is not None: if t[2] is not None:
half=t[2].strip() half = t[2].strip()
return numbers + half return numbers + half
def normalize_text(text: str,normalization_options: NormalizationOptions) -> str:
def normalize_text(text: str, normalization_options: NormalizationOptions) -> str:
"""Normalize text for TTS processing""" """Normalize text for TTS processing"""
# Handle email addresses first if enabled # Handle email addresses first if enabled
if normalization_options.email_normalization: if normalization_options.email_normalization:
@ -269,15 +342,19 @@ def normalize_text(text: str,normalization_options: NormalizationOptions) -> str
# Pre-process numbers with units if enabled # Pre-process numbers with units if enabled
if normalization_options.unit_normalization: if normalization_options.unit_normalization:
text=UNIT_PATTERN.sub(handle_units,text) text = UNIT_PATTERN.sub(handle_units, text)
# Replace optional pluralization # Replace optional pluralization
if normalization_options.optional_pluralization_normalization: if normalization_options.optional_pluralization_normalization:
text = re.sub(r"\(s\)","s",text) text = re.sub(r"\(s\)", "s", text)
# Replace phone numbers: # Replace phone numbers:
if normalization_options.phone_normalization: if normalization_options.phone_normalization:
text = re.sub(r"(\+?\d{1,2})?([ .-]?)(\(?\d{3}\)?)[\s.-](\d{3})[\s.-](\d{4})",handle_phone_number,text) text = re.sub(
r"(\+?\d{1,2})?([ .-]?)(\(?\d{3}\)?)[\s.-](\d{3})[\s.-](\d{4})",
handle_phone_number,
text,
)
# Replace quotes and brackets # Replace quotes and brackets
text = text.replace(chr(8216), "'").replace(chr(8217), "'") text = text.replace(chr(8216), "'").replace(chr(8217), "'")
@ -289,7 +366,10 @@ def normalize_text(text: str,normalization_options: NormalizationOptions) -> str
text = text.replace(a, b + " ") text = text.replace(a, b + " ")
# Handle simple time in the format of HH:MM:SS # Handle simple time in the format of HH:MM:SS
text = TIME_PATTERN.sub(handle_time, text, ) text = TIME_PATTERN.sub(
handle_time,
text,
)
# Clean up whitespace # Clean up whitespace
text = re.sub(r"[^\S \n]", " ", text) text = re.sub(r"[^\S \n]", " ", text)
@ -329,6 +409,6 @@ def normalize_text(text: str,normalization_options: NormalizationOptions) -> str
text = re.sub( text = re.sub(
r"(?:[A-Za-z]\.){2,} [a-z]", lambda m: m.group().replace(".", "-"), text r"(?:[A-Za-z]\.){2,} [a-z]", lambda m: m.group().replace(".", "-"), text
) )
text = re.sub( r"(?i)(?<=[A-Z])\.(?=[A-Z])", "-", text) text = re.sub(r"(?i)(?<=[A-Z])\.(?=[A-Z])", "-", text)
return text.strip() return text.strip()

32
api/src/services/text_processing/text_processor.py
View file

@ -15,6 +15,7 @@ from .vocabulary import tokenize
# Pre-compiled regex patterns for performance # Pre-compiled regex patterns for performance
CUSTOM_PHONEMES = re.compile(r"(\[([^\]]|\n)*?\])(\(\/([^\/)]|\n)*?\/\))") CUSTOM_PHONEMES = re.compile(r"(\[([^\]]|\n)*?\])(\(\/([^\/)]|\n)*?\/\))")
def process_text_chunk( def process_text_chunk(
text: str, language: str = "a", skip_phonemize: bool = False text: str, language: str = "a", skip_phonemize: bool = False
) -> List[int]: ) -> List[int]:
@ -41,9 +42,7 @@ def process_text_chunk(
t1 = time.time() t1 = time.time()
t0 = time.time() t0 = time.time()
phonemes = phonemize( phonemes = phonemize(text, language, normalize=False) # Already normalized
text, language, normalize=False
) # Already normalized
t1 = time.time() t1 = time.time()
t0 = time.time() t0 = time.time()
@ -88,7 +87,9 @@ def process_text(text: str, language: str = "a") -> List[int]:
return process_text_chunk(text, language) return process_text_chunk(text, language)
def get_sentence_info(text: str, custom_phenomes_list: Dict[str, str]) -> List[Tuple[str, List[int], int]]: def get_sentence_info(
text: str, custom_phenomes_list: Dict[str, str]
) -> List[Tuple[str, List[int], int]]:
"""Process all sentences and return info.""" """Process all sentences and return info."""
sentences = re.split(r"([.!?;:])(?=\s|$)", text) sentences = re.split(r"([.!?;:])(?=\s|$)", text)
phoneme_length, min_value = len(custom_phenomes_list), 0 phoneme_length, min_value = len(custom_phenomes_list), 0
@ -99,10 +100,11 @@ def get_sentence_info(text: str, custom_phenomes_list: Dict[str, str]) -> List[T
for replaced in range(min_value, phoneme_length): for replaced in range(min_value, phoneme_length):
current_id = f"</|custom_phonemes_{replaced}|/>" current_id = f"</|custom_phonemes_{replaced}|/>"
if current_id in sentence: if current_id in sentence:
sentence = sentence.replace(current_id, custom_phenomes_list.pop(current_id)) sentence = sentence.replace(
current_id, custom_phenomes_list.pop(current_id)
)
min_value += 1 min_value += 1
punct = sentences[i + 1] if i + 1 < len(sentences) else "" punct = sentences[i + 1] if i + 1 < len(sentences) else ""
if not sentence: if not sentence:
@ -114,16 +116,18 @@ def get_sentence_info(text: str, custom_phenomes_list: Dict[str, str]) -> List[T
return results return results
def handle_custom_phonemes(s: re.Match[str], phenomes_list: Dict[str,str]) -> str:
def handle_custom_phonemes(s: re.Match[str], phenomes_list: Dict[str, str]) -> str:
latest_id = f"</|custom_phonemes_{len(phenomes_list)}|/>" latest_id = f"</|custom_phonemes_{len(phenomes_list)}|/>"
phenomes_list[latest_id] = s.group(0).strip() phenomes_list[latest_id] = s.group(0).strip()
return latest_id return latest_id
async def smart_split( async def smart_split(
text: str, text: str,
max_tokens: int = settings.absolute_max_tokens, max_tokens: int = settings.absolute_max_tokens,
lang_code: str = "a", lang_code: str = "a",
normalization_options: NormalizationOptions = NormalizationOptions() normalization_options: NormalizationOptions = NormalizationOptions(),
) -> AsyncGenerator[Tuple[str, List[int]], None]: ) -> AsyncGenerator[Tuple[str, List[int]], None]:
"""Build optimal chunks targeting 300-400 tokens, never exceeding max_tokens.""" """Build optimal chunks targeting 300-400 tokens, never exceeding max_tokens."""
start_time = time.time() start_time = time.time()
@ -135,11 +139,15 @@ async def smart_split(
# Normalize text # Normalize text
if settings.advanced_text_normalization and normalization_options.normalize: if settings.advanced_text_normalization and normalization_options.normalize:
print(lang_code) print(lang_code)
if lang_code in ["a","b","en-us","en-gb"]: if lang_code in ["a", "b", "en-us", "en-gb"]:
text = CUSTOM_PHONEMES.sub(lambda s: handle_custom_phonemes(s, custom_phoneme_list), text) text = CUSTOM_PHONEMES.sub(
text=normalize_text(text,normalization_options) lambda s: handle_custom_phonemes(s, custom_phoneme_list), text
)
text = normalize_text(text, normalization_options)
else: else:
logger.info("Skipping text normalization as it is only supported for english") logger.info(
"Skipping text normalization as it is only supported for english"
)
# Process all sentences # Process all sentences
sentences = get_sentence_info(text, custom_phoneme_list) sentences = get_sentence_info(text, custom_phoneme_list)

80
api/src/services/tts_service.py
View file

@ -69,7 +69,9 @@ class TTSService:
yield AudioChunk(np.array([], dtype=np.int16), output=b"") yield AudioChunk(np.array([], dtype=np.int16), output=b"")
return return
chunk_data = await AudioService.convert_audio( chunk_data = await AudioService.convert_audio(
AudioChunk(np.array([], dtype=np.float32)), # Dummy data for type checking AudioChunk(
np.array([], dtype=np.float32)
), # Dummy data for type checking
output_format, output_format,
writer, writer,
speed, speed,
@ -114,13 +116,22 @@ class TTSService:
except Exception as e: except Exception as e:
logger.error(f"Failed to convert audio: {str(e)}") logger.error(f"Failed to convert audio: {str(e)}")
else: else:
chunk_data = AudioService.trim_audio(chunk_data, chunk_text, speed, is_last, normalizer) chunk_data = AudioService.trim_audio(
chunk_data, chunk_text, speed, is_last, normalizer
)
yield chunk_data yield chunk_data
chunk_index += 1 chunk_index += 1
else: else:
# For legacy backends, load voice tensor # For legacy backends, load voice tensor
voice_tensor = await self._voice_manager.load_voice(voice_name, device=backend.device) voice_tensor = await self._voice_manager.load_voice(
chunk_data = await self.model_manager.generate(tokens, voice_tensor, speed=speed, return_timestamps=return_timestamps) voice_name, device=backend.device
)
chunk_data = await self.model_manager.generate(
tokens,
voice_tensor,
speed=speed,
return_timestamps=return_timestamps,
)
if chunk_data.audio is None: if chunk_data.audio is None:
logger.error("Model generated None for audio chunk") logger.error("Model generated None for audio chunk")
@ -146,7 +157,9 @@ class TTSService:
except Exception as e: except Exception as e:
logger.error(f"Failed to convert audio: {str(e)}") logger.error(f"Failed to convert audio: {str(e)}")
else: else:
trimmed = AudioService.trim_audio(chunk_data, chunk_text, speed, is_last, normalizer) trimmed = AudioService.trim_audio(
chunk_data, chunk_text, speed, is_last, normalizer
)
yield trimmed yield trimmed
except Exception as e: except Exception as e:
logger.error(f"Failed to process tokens: {str(e)}") logger.error(f"Failed to process tokens: {str(e)}")
@ -178,7 +191,9 @@ class TTSService:
# If it is only once voice there is no point in loading it up, doing nothing with it, then saving it # If it is only once voice there is no point in loading it up, doing nothing with it, then saving it
if len(split_voice) == 1: if len(split_voice) == 1:
# Since its a single voice the only time that the weight would matter is if voice_weight_normalization is off # Since its a single voice the only time that the weight would matter is if voice_weight_normalization is off
if ("(" not in voice and ")" not in voice) or settings.voice_weight_normalization == True: if (
"(" not in voice and ")" not in voice
) or settings.voice_weight_normalization == True:
path = await self._voice_manager.get_voice_path(voice) path = await self._voice_manager.get_voice_path(voice)
if not path: if not path:
raise RuntimeError(f"Voice not found: {voice}") raise RuntimeError(f"Voice not found: {voice}")
@ -206,13 +221,19 @@ class TTSService:
# Load the first voice as the starting point for voices to be combined onto # Load the first voice as the starting point for voices to be combined onto
path = await self._voice_manager.get_voice_path(split_voice[0][0]) path = await self._voice_manager.get_voice_path(split_voice[0][0])
combined_tensor = await self._load_voice_from_path(path, split_voice[0][1] / total_weight) combined_tensor = await self._load_voice_from_path(
path, split_voice[0][1] / total_weight
)
# Loop through each + or - in split_voice so they can be applied to combined voice # Loop through each + or - in split_voice so they can be applied to combined voice
for operation_index in range(1, len(split_voice) - 1, 2): for operation_index in range(1, len(split_voice) - 1, 2):
# Get the voice path of the voice 1 index ahead of the operator # Get the voice path of the voice 1 index ahead of the operator
path = await self._voice_manager.get_voice_path(split_voice[operation_index + 1][0]) path = await self._voice_manager.get_voice_path(
voice_tensor = await self._load_voice_from_path(path, split_voice[operation_index + 1][1] / total_weight) split_voice[operation_index + 1][0]
)
voice_tensor = await self._load_voice_from_path(
path, split_voice[operation_index + 1][1] / total_weight
)
# Either add or subtract the voice from the current combined voice # Either add or subtract the voice from the current combined voice
if split_voice[operation_index] == "+": if split_voice[operation_index] == "+":
@ -255,10 +276,16 @@ class TTSService:
# Use provided lang_code or determine from voice name # Use provided lang_code or determine from voice name
pipeline_lang_code = lang_code if lang_code else voice[:1].lower() pipeline_lang_code = lang_code if lang_code else voice[:1].lower()
logger.info(f"Using lang_code '{pipeline_lang_code}' for voice '{voice_name}' in audio stream") logger.info(
f"Using lang_code '{pipeline_lang_code}' for voice '{voice_name}' in audio stream"
)
# Process text in chunks with smart splitting # Process text in chunks with smart splitting
async for chunk_text, tokens in smart_split(text, lang_code=pipeline_lang_code, normalization_options=normalization_options): async for chunk_text, tokens in smart_split(
text,
lang_code=pipeline_lang_code,
normalization_options=normalization_options,
):
try: try:
# Process audio for chunk # Process audio for chunk
async for chunk_data in self._process_chunk( async for chunk_data in self._process_chunk(
@ -286,10 +313,14 @@ class TTSService:
yield chunk_data yield chunk_data
else: else:
logger.warning(f"No audio generated for chunk: '{chunk_text[:100]}...'") logger.warning(
f"No audio generated for chunk: '{chunk_text[:100]}...'"
)
chunk_index += 1 chunk_index += 1
except Exception as e: except Exception as e:
logger.error(f"Failed to process audio for chunk: '{chunk_text[:100]}...'. Error: {str(e)}") logger.error(
f"Failed to process audio for chunk: '{chunk_text[:100]}...'. Error: {str(e)}"
)
continue continue
# Only finalize if we successfully processed at least one chunk # Only finalize if we successfully processed at least one chunk
@ -332,7 +363,16 @@ class TTSService:
audio_data_chunks = [] audio_data_chunks = []
try: try:
async for audio_stream_data in self.generate_audio_stream(text, voice, writer, speed=speed, normalization_options=normalization_options, return_timestamps=return_timestamps, lang_code=lang_code, output_format=None): async for audio_stream_data in self.generate_audio_stream(
text,
voice,
writer,
speed=speed,
normalization_options=normalization_options,
return_timestamps=return_timestamps,
lang_code=lang_code,
output_format=None,
):
if len(audio_stream_data.audio) > 0: if len(audio_stream_data.audio) > 0:
audio_data_chunks.append(audio_stream_data) audio_data_chunks.append(audio_stream_data)
@ -384,11 +424,15 @@ class TTSService:
result = None result = None
# Use provided lang_code or determine from voice name # Use provided lang_code or determine from voice name
pipeline_lang_code = lang_code if lang_code else voice[:1].lower() pipeline_lang_code = lang_code if lang_code else voice[:1].lower()
logger.info(f"Using lang_code '{pipeline_lang_code}' for voice '{voice_name}' in phoneme pipeline") logger.info(
f"Using lang_code '{pipeline_lang_code}' for voice '{voice_name}' in phoneme pipeline"
)
try: try:
# Use backend's pipeline management # Use backend's pipeline management
for r in backend._get_pipeline(pipeline_lang_code).generate_from_tokens( for r in backend._get_pipeline(
pipeline_lang_code
).generate_from_tokens(
tokens=phonemes, # Pass raw phonemes string tokens=phonemes, # Pass raw phonemes string
voice=voice_path, voice=voice_path,
speed=speed, speed=speed,
@ -406,7 +450,9 @@ class TTSService:
processing_time = time.time() - start_time processing_time = time.time() - start_time
return result.audio.numpy(), processing_time return result.audio.numpy(), processing_time
else: else:
raise ValueError("Phoneme generation only supported with Kokoro V1 backend") raise ValueError(
"Phoneme generation only supported with Kokoro V1 backend"
)
except Exception as e: except Exception as e:
logger.error(f"Error in phoneme audio generation: {str(e)}") logger.error(f"Error in phoneme audio generation: {str(e)}")

11
api/src/structures/custom_responses.py
View file

@ -24,16 +24,12 @@ class JSONStreamingResponse(StreamingResponse, JSONResponse):
else: else:
self._content_iterable = iterate_in_threadpool(content) self._content_iterable = iterate_in_threadpool(content)
async def body_iterator() -> AsyncIterable[bytes]: async def body_iterator() -> AsyncIterable[bytes]:
async for content_ in self._content_iterable: async for content_ in self._content_iterable:
if isinstance(content_, BaseModel): if isinstance(content_, BaseModel):
content_ = content_.model_dump() content_ = content_.model_dump()
yield self.render(content_) yield self.render(content_)
self.body_iterator = body_iterator() self.body_iterator = body_iterator()
self.status_code = status_code self.status_code = status_code
if media_type is not None: if media_type is not None:
@ -42,10 +38,13 @@ class JSONStreamingResponse(StreamingResponse, JSONResponse):
self.init_headers(headers) self.init_headers(headers)
def render(self, content: typing.Any) -> bytes: def render(self, content: typing.Any) -> bytes:
return (json.dumps( return (
json.dumps(
content, content,
ensure_ascii=False, ensure_ascii=False,
allow_nan=False, allow_nan=False,
indent=None, indent=None,
separators=(",", ":"), separators=(",", ":"),
) + "\n").encode("utf-8") )
+ "\n"
).encode("utf-8")

52
api/src/structures/schemas.py
View file

@ -35,16 +35,38 @@ class CaptionedSpeechResponse(BaseModel):
audio: str = Field(..., description="The generated audio data encoded in base 64") audio: str = Field(..., description="The generated audio data encoded in base 64")
audio_format: str = Field(..., description="The format of the output audio") audio_format: str = Field(..., description="The format of the output audio")
timestamps: Optional[List[WordTimestamp]] = Field(..., description="Word-level timestamps") timestamps: Optional[List[WordTimestamp]] = Field(
..., description="Word-level timestamps"
)
class NormalizationOptions(BaseModel): class NormalizationOptions(BaseModel):
"""Options for the normalization system""" """Options for the normalization system"""
normalize: bool = Field(default=True, description="Normalizes input text to make it easier for the model to say")
unit_normalization: bool = Field(default=False,description="Transforms units like 10KB to 10 kilobytes") normalize: bool = Field(
url_normalization: bool = Field(default=True, description="Changes urls so they can be properly pronouced by kokoro") default=True,
email_normalization: bool = Field(default=True, description="Changes emails so they can be properly pronouced by kokoro") description="Normalizes input text to make it easier for the model to say",
optional_pluralization_normalization: bool = Field(default=True, description="Replaces (s) with s so some words get pronounced correctly") )
phone_normalization: bool = Field(default=True, description="Changes phone numbers so they can be properly pronouced by kokoro") unit_normalization: bool = Field(
default=False, description="Transforms units like 10KB to 10 kilobytes"
)
url_normalization: bool = Field(
default=True,
description="Changes urls so they can be properly pronouced by kokoro",
)
email_normalization: bool = Field(
default=True,
description="Changes emails so they can be properly pronouced by kokoro",
)
optional_pluralization_normalization: bool = Field(
default=True,
description="Replaces (s) with s so some words get pronounced correctly",
)
phone_normalization: bool = Field(
default=True,
description="Changes phone numbers so they can be properly pronouced by kokoro",
)
class OpenAISpeechRequest(BaseModel): class OpenAISpeechRequest(BaseModel):
"""Request schema for OpenAI-compatible speech endpoint""" """Request schema for OpenAI-compatible speech endpoint"""
@ -62,9 +84,11 @@ class OpenAISpeechRequest(BaseModel):
default="mp3", default="mp3",
description="The format to return audio in. Supported formats: mp3, opus, flac, wav, pcm. PCM format returns raw 16-bit samples without headers. AAC is not currently supported.", description="The format to return audio in. Supported formats: mp3, opus, flac, wav, pcm. PCM format returns raw 16-bit samples without headers. AAC is not currently supported.",
) )
download_format: Optional[Literal["mp3", "opus", "aac", "flac", "wav", "pcm"]] = Field( download_format: Optional[Literal["mp3", "opus", "aac", "flac", "wav", "pcm"]] = (
default=None, Field(
description="Optional different format for the final download. If not provided, uses response_format.", default=None,
description="Optional different format for the final download. If not provided, uses response_format.",
)
) )
speed: float = Field( speed: float = Field(
default=1.0, default=1.0,
@ -85,8 +109,8 @@ class OpenAISpeechRequest(BaseModel):
description="Optional language code to use for text processing. If not provided, will use first letter of voice name.", description="Optional language code to use for text processing. If not provided, will use first letter of voice name.",
) )
normalization_options: Optional[NormalizationOptions] = Field( normalization_options: Optional[NormalizationOptions] = Field(
default= NormalizationOptions(), default=NormalizationOptions(),
description= "Options for the normalization system" description="Options for the normalization system",
) )
@ -129,6 +153,6 @@ class CaptionedSpeechRequest(BaseModel):
description="Optional language code to use for text processing. If not provided, will use first letter of voice name.", description="Optional language code to use for text processing. If not provided, will use first letter of voice name.",
) )
normalization_options: Optional[NormalizationOptions] = Field( normalization_options: Optional[NormalizationOptions] = Field(
default= NormalizationOptions(), default=NormalizationOptions(),
description= "Options for the normalization system" description="Options for the normalization system",
) )

1
api/tests/conftest.py
View file

@ -69,4 +69,3 @@ async def tts_service(mock_model_manager, mock_voice_manager):
def test_voice(): def test_voice():
"""Return a test voice name.""" """Return a test voice name."""
return "voice1" return "voice1"

13
api/tests/test_audio_service.py
View file

@ -66,7 +66,9 @@ async def test_convert_to_mp3(sample_audio):
assert isinstance(audio_chunk, AudioChunk) assert isinstance(audio_chunk, AudioChunk)
assert len(audio_chunk.output) > 0 assert len(audio_chunk.output) > 0
# Check MP3 header (ID3 or MPEG frame sync) # Check MP3 header (ID3 or MPEG frame sync)
assert audio_chunk.output.startswith(b"ID3") or audio_chunk.output.startswith(b"\xff\xfb") assert audio_chunk.output.startswith(b"ID3") or audio_chunk.output.startswith(
b"\xff\xfb"
)
@pytest.mark.asyncio @pytest.mark.asyncio
@ -78,7 +80,7 @@ async def test_convert_to_opus(sample_audio):
writer = StreamingAudioWriter("opus", sample_rate=24000) writer = StreamingAudioWriter("opus", sample_rate=24000)
audio_chunk = await AudioService.convert_audio( audio_chunk = await AudioService.convert_audio(
AudioChunk(audio_data), "opus",writer AudioChunk(audio_data), "opus", writer
) )
writer.close() writer.close()
@ -127,7 +129,9 @@ async def test_convert_to_aac(sample_audio):
assert isinstance(audio_chunk, AudioChunk) assert isinstance(audio_chunk, AudioChunk)
assert len(audio_chunk.output) > 0 assert len(audio_chunk.output) > 0
# Check ADTS header (AAC) # Check ADTS header (AAC)
assert audio_chunk.output.startswith(b"\xff\xf0") or audio_chunk.output.startswith(b"\xff\xf1") assert audio_chunk.output.startswith(b"\xff\xf0") or audio_chunk.output.startswith(
b"\xff\xf1"
)
@pytest.mark.asyncio @pytest.mark.asyncio
@ -152,7 +156,7 @@ async def test_convert_to_pcm(sample_audio):
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_convert_to_invalid_format_raises_error(sample_audio): async def test_convert_to_invalid_format_raises_error(sample_audio):
"""Test that converting to an invalid format raises an error""" """Test that converting to an invalid format raises an error"""
#audio_data, sample_rate = sample_audio # audio_data, sample_rate = sample_audio
with pytest.raises(ValueError, match="Unsupported format: invalid"): with pytest.raises(ValueError, match="Unsupported format: invalid"):
writer = StreamingAudioWriter("invalid", sample_rate=24000) writer = StreamingAudioWriter("invalid", sample_rate=24000)
@ -214,7 +218,6 @@ async def test_different_sample_rates(sample_audio):
sample_rates = [8000, 16000, 44100, 48000] sample_rates = [8000, 16000, 44100, 48000]
for rate in sample_rates: for rate in sample_rates:
writer = StreamingAudioWriter("wav", sample_rate=rate) writer = StreamingAudioWriter("wav", sample_rate=rate)
audio_chunk = await AudioService.convert_audio( audio_chunk = await AudioService.convert_audio(

13
api/tests/test_development.py
View file

@ -14,14 +14,15 @@ def test_generate_captioned_speech():
mock_timestamps_response = MagicMock() mock_timestamps_response = MagicMock()
mock_timestamps_response.status_code = 200 mock_timestamps_response.status_code = 200
mock_timestamps_response.content = json.dumps({ mock_timestamps_response.content = json.dumps(
"audio":base64.b64encode(b"mock audio data").decode("utf-8"), {
"timestamps":[{"word": "test", "start_time": 0.0, "end_time": 1.0}] "audio": base64.b64encode(b"mock audio data").decode("utf-8"),
}) "timestamps": [{"word": "test", "start_time": 0.0, "end_time": 1.0}],
}
)
# Patch the HTTP requests # Patch the HTTP requests
with patch('requests.post', return_value=mock_timestamps_response): with patch("requests.post", return_value=mock_timestamps_response):
# Import here to avoid module-level import issues # Import here to avoid module-level import issues
from examples.captioned_speech_example import generate_captioned_speech from examples.captioned_speech_example import generate_captioned_speech

130
api/tests/test_normalizer.py
View file

@ -9,24 +9,44 @@ from api.src.structures.schemas import NormalizationOptions
def test_url_protocols(): def test_url_protocols():
"""Test URL protocol handling""" """Test URL protocol handling"""
assert ( assert (
normalize_text("Check out https://example.com",normalization_options=NormalizationOptions()) normalize_text(
"Check out https://example.com",
normalization_options=NormalizationOptions(),
)
== "Check out https example dot com" == "Check out https example dot com"
) )
assert normalize_text("Visit http://site.com",normalization_options=NormalizationOptions()) == "Visit http site dot com"
assert ( assert (
normalize_text("Go to https://test.org/path",normalization_options=NormalizationOptions()) normalize_text(
"Visit http://site.com", normalization_options=NormalizationOptions()
)
== "Visit http site dot com"
)
assert (
normalize_text(
"Go to https://test.org/path", normalization_options=NormalizationOptions()
)
== "Go to https test dot org slash path" == "Go to https test dot org slash path"
) )
def test_url_www(): def test_url_www():
"""Test www prefix handling""" """Test www prefix handling"""
assert normalize_text("Go to www.example.com",normalization_options=NormalizationOptions()) == "Go to www example dot com"
assert ( assert (
normalize_text("Visit www.test.org/docs",normalization_options=NormalizationOptions()) == "Visit www test dot org slash docs" normalize_text(
"Go to www.example.com", normalization_options=NormalizationOptions()
)
== "Go to www example dot com"
) )
assert ( assert (
normalize_text("Check www.site.com?q=test",normalization_options=NormalizationOptions()) normalize_text(
"Visit www.test.org/docs", normalization_options=NormalizationOptions()
)
== "Visit www test dot org slash docs"
)
assert (
normalize_text(
"Check www.site.com?q=test", normalization_options=NormalizationOptions()
)
== "Check www site dot com question-mark q equals test" == "Check www site dot com question-mark q equals test"
) )
@ -34,15 +54,21 @@ def test_url_www():
def test_url_localhost(): def test_url_localhost():
"""Test localhost URL handling""" """Test localhost URL handling"""
assert ( assert (
normalize_text("Running on localhost:7860",normalization_options=NormalizationOptions()) normalize_text(
"Running on localhost:7860", normalization_options=NormalizationOptions()
)
== "Running on localhost colon 78 60" == "Running on localhost colon 78 60"
) )
assert ( assert (
normalize_text("Server at localhost:8080/api",normalization_options=NormalizationOptions()) normalize_text(
"Server at localhost:8080/api", normalization_options=NormalizationOptions()
)
== "Server at localhost colon 80 80 slash api" == "Server at localhost colon 80 80 slash api"
) )
assert ( assert (
normalize_text("Test localhost:3000/test?v=1",normalization_options=NormalizationOptions()) normalize_text(
"Test localhost:3000/test?v=1", normalization_options=NormalizationOptions()
)
== "Test localhost colon 3000 slash test question-mark v equals 1" == "Test localhost colon 3000 slash test question-mark v equals 1"
) )
@ -50,48 +76,104 @@ def test_url_localhost():
def test_url_ip_addresses(): def test_url_ip_addresses():
"""Test IP address URL handling""" """Test IP address URL handling"""
assert ( assert (
normalize_text("Access 0.0.0.0:9090/test",normalization_options=NormalizationOptions()) normalize_text(
"Access 0.0.0.0:9090/test", normalization_options=NormalizationOptions()
)
== "Access 0 dot 0 dot 0 dot 0 colon 90 90 slash test" == "Access 0 dot 0 dot 0 dot 0 colon 90 90 slash test"
) )
assert ( assert (
normalize_text("API at 192.168.1.1:8000",normalization_options=NormalizationOptions()) normalize_text(
"API at 192.168.1.1:8000", normalization_options=NormalizationOptions()
)
== "API at 192 dot 168 dot 1 dot 1 colon 8000" == "API at 192 dot 168 dot 1 dot 1 colon 8000"
) )
assert normalize_text("Server 127.0.0.1",normalization_options=NormalizationOptions()) == "Server 127 dot 0 dot 0 dot 1" assert (
normalize_text("Server 127.0.0.1", normalization_options=NormalizationOptions())
== "Server 127 dot 0 dot 0 dot 1"
)
def test_url_raw_domains(): def test_url_raw_domains():
"""Test raw domain handling""" """Test raw domain handling"""
assert ( assert (
normalize_text("Visit google.com/search",normalization_options=NormalizationOptions()) == "Visit google dot com slash search" normalize_text(
"Visit google.com/search", normalization_options=NormalizationOptions()
)
== "Visit google dot com slash search"
) )
assert ( assert (
normalize_text("Go to example.com/path?q=test",normalization_options=NormalizationOptions()) normalize_text(
"Go to example.com/path?q=test",
normalization_options=NormalizationOptions(),
)
== "Go to example dot com slash path question-mark q equals test" == "Go to example dot com slash path question-mark q equals test"
) )
assert normalize_text("Check docs.test.com",normalization_options=NormalizationOptions()) == "Check docs dot test dot com" assert (
normalize_text(
"Check docs.test.com", normalization_options=NormalizationOptions()
)
== "Check docs dot test dot com"
)
def test_url_email_addresses(): def test_url_email_addresses():
"""Test email address handling""" """Test email address handling"""
assert ( assert (
normalize_text("Email me at user@example.com",normalization_options=NormalizationOptions()) normalize_text(
"Email me at user@example.com", normalization_options=NormalizationOptions()
)
== "Email me at user at example dot com" == "Email me at user at example dot com"
) )
assert normalize_text("Contact admin@test.org",normalization_options=NormalizationOptions()) == "Contact admin at test dot org"
assert ( assert (
normalize_text("Send to test.user@site.com",normalization_options=NormalizationOptions()) normalize_text(
"Contact admin@test.org", normalization_options=NormalizationOptions()
)
== "Contact admin at test dot org"
)
assert (
normalize_text(
"Send to test.user@site.com", normalization_options=NormalizationOptions()
)
== "Send to test dot user at site dot com" == "Send to test dot user at site dot com"
) )
def test_money(): def test_money():
"""Test that money text is normalized correctly""" """Test that money text is normalized correctly"""
assert normalize_text("He lost $5.3 thousand.",normalization_options=NormalizationOptions()) == "He lost five point three thousand dollars." assert (
assert normalize_text("To put it weirdly -$6.9 million",normalization_options=NormalizationOptions()) == "To put it weirdly minus six point nine million dollars" normalize_text(
assert normalize_text("It costs $50.3.",normalization_options=NormalizationOptions()) == "It costs fifty dollars and thirty cents." "He lost $5.3 thousand.", normalization_options=NormalizationOptions()
)
== "He lost five point three thousand dollars."
)
assert (
normalize_text(
"To put it weirdly -$6.9 million",
normalization_options=NormalizationOptions(),
)
== "To put it weirdly minus six point nine million dollars"
)
assert (
normalize_text("It costs $50.3.", normalization_options=NormalizationOptions())
== "It costs fifty dollars and thirty cents."
)
def test_non_url_text(): def test_non_url_text():
"""Test that non-URL text is unaffected""" """Test that non-URL text is unaffected"""
assert normalize_text("This is not.a.url text",normalization_options=NormalizationOptions()) == "This is not-a-url text" assert (
assert normalize_text("Hello, how are you today?",normalization_options=NormalizationOptions()) == "Hello, how are you today?" normalize_text(
assert normalize_text("It costs $50.",normalization_options=NormalizationOptions()) == "It costs fifty dollars." "This is not.a.url text", normalization_options=NormalizationOptions()
)
== "This is not-a-url text"
)
assert (
normalize_text(
"Hello, how are you today?", normalization_options=NormalizationOptions()
)
== "Hello, how are you today?"
)
assert (
normalize_text("It costs $50.", normalization_options=NormalizationOptions())
== "It costs fifty dollars."
)

13
api/tests/test_openai_endpoints.py
View file

@ -113,7 +113,6 @@ def test_retrieve_model(mock_openai_mappings):
assert error["detail"]["type"] == "invalid_request_error" assert error["detail"]["type"] == "invalid_request_error"
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_get_tts_service_initialization(): async def test_get_tts_service_initialization():
"""Test TTSService initialization""" """Test TTSService initialization"""
@ -146,7 +145,7 @@ async def test_stream_audio_chunks_client_disconnect():
async def mock_stream(*args, **kwargs): async def mock_stream(*args, **kwargs):
for i in range(5): for i in range(5):
yield AudioChunk(np.ndarray([],np.int16),output=b"chunk") yield AudioChunk(np.ndarray([], np.int16), output=b"chunk")
mock_service.generate_audio_stream = mock_stream mock_service.generate_audio_stream = mock_stream
mock_service.list_voices.return_value = ["test_voice"] mock_service.list_voices.return_value = ["test_voice"]
@ -242,10 +241,10 @@ def mock_tts_service(mock_audio_bytes):
"""Mock TTS service for testing.""" """Mock TTS service for testing."""
with patch("api.src.routers.openai_compatible.get_tts_service") as mock_get: with patch("api.src.routers.openai_compatible.get_tts_service") as mock_get:
service = AsyncMock(spec=TTSService) service = AsyncMock(spec=TTSService)
service.generate_audio.return_value = AudioChunk(np.zeros(1000,np.int16)) service.generate_audio.return_value = AudioChunk(np.zeros(1000, np.int16))
async def mock_stream(*args, **kwargs) -> AsyncGenerator[AudioChunk, None]: async def mock_stream(*args, **kwargs) -> AsyncGenerator[AudioChunk, None]:
yield AudioChunk(np.ndarray([],np.int16),output=mock_audio_bytes) yield AudioChunk(np.ndarray([], np.int16), output=mock_audio_bytes)
service.generate_audio_stream = mock_stream service.generate_audio_stream = mock_stream
service.list_voices.return_value = ["test_voice", "voice1", "voice2"] service.list_voices.return_value = ["test_voice", "voice1", "voice2"]
@ -262,8 +261,10 @@ def test_openai_speech_endpoint(
): ):
"""Test the OpenAI-compatible speech endpoint with basic MP3 generation""" """Test the OpenAI-compatible speech endpoint with basic MP3 generation"""
# Configure mocks # Configure mocks
mock_tts_service.generate_audio.return_value = AudioChunk(np.zeros(1000,np.int16)) mock_tts_service.generate_audio.return_value = AudioChunk(np.zeros(1000, np.int16))
mock_convert.return_value = AudioChunk(np.zeros(1000,np.int16),output=mock_audio_bytes) mock_convert.return_value = AudioChunk(
np.zeros(1000, np.int16), output=mock_audio_bytes
)
response = client.post( response = client.post(
"/v1/audio/speech", "/v1/audio/speech",

6
api/tests/test_text_processor.py
View file

@ -44,9 +44,12 @@ def test_get_sentence_info():
assert count == len(tokens) assert count == len(tokens)
assert count > 0 assert count > 0
def test_get_sentence_info_phenomoes(): def test_get_sentence_info_phenomoes():
"""Test sentence splitting and info extraction.""" """Test sentence splitting and info extraction."""
text = "This is sentence one. This is </|custom_phonemes_0|/> two! What about three?" text = (
"This is sentence one. This is </|custom_phonemes_0|/> two! What about three?"
)
results = get_sentence_info(text, {"</|custom_phonemes_0|/>": r"sˈɛntᵊns"}) results = get_sentence_info(text, {"</|custom_phonemes_0|/>": r"sˈɛntᵊns"})
assert len(results) == 3 assert len(results) == 3
@ -58,6 +61,7 @@ def test_get_sentence_info_phenomoes():
assert count == len(tokens) assert count == len(tokens)
assert count > 0 assert count > 0
@pytest.mark.asyncio @pytest.mark.asyncio
async def test_smart_split_short_text(): async def test_smart_split_short_text():
"""Test smart splitting with text under max tokens.""" """Test smart splitting with text under max tokens."""

208
dev/Test Threads.py
View file

@ -28,25 +28,33 @@ import requests
def setup_args(): def setup_args():
"""Parse command line arguments""" """Parse command line arguments"""
parser = argparse.ArgumentParser(description="Test Kokoro TTS for race conditions") parser = argparse.ArgumentParser(description="Test Kokoro TTS for race conditions")
parser.add_argument("--url", default="http://localhost:8880", parser.add_argument(
help="Base URL of the Kokoro TTS service") "--url",
parser.add_argument("--threads", type=int, default=8, default="http://localhost:8880",
help="Number of concurrent threads to use") help="Base URL of the Kokoro TTS service",
parser.add_argument("--iterations", type=int, default=5, )
help="Number of iterations per thread") parser.add_argument(
parser.add_argument("--voice", default="af_heart", "--threads", type=int, default=8, help="Number of concurrent threads to use"
help="Voice to use for TTS") )
parser.add_argument("--output-dir", default="./tts_test_output", parser.add_argument(
help="Directory to save output files") "--iterations", type=int, default=5, help="Number of iterations per thread"
parser.add_argument("--debug", action="store_true", )
help="Enable debug logging") parser.add_argument("--voice", default="af_heart", help="Voice to use for TTS")
parser.add_argument(
"--output-dir",
default="./tts_test_output",
help="Directory to save output files",
)
parser.add_argument("--debug", action="store_true", help="Enable debug logging")
return parser.parse_args() return parser.parse_args()
def generate_test_sentence(thread_id, iteration): def generate_test_sentence(thread_id, iteration):
"""Generate a simple test sentence with numbers to make mismatches easily identifiable""" """Generate a simple test sentence with numbers to make mismatches easily identifiable"""
return f"This is test sentence number {thread_id}-{iteration}. " \ return (
f"If you hear this sentence, you should hear the numbers {thread_id}-{iteration}." f"This is test sentence number {thread_id}-{iteration}. "
f"If you hear this sentence, you should hear the numbers {thread_id}-{iteration}."
)
def log_message(message, debug=False, is_error=False): def log_message(message, debug=False, is_error=False):
@ -74,7 +82,9 @@ def request_tts(url, test_id, text, voice, output_dir, debug=False):
f.write(text) f.write(text)
log_message(f"Thread {test_id}: Successfully saved text file", debug) log_message(f"Thread {test_id}: Successfully saved text file", debug)
except Exception as e: except Exception as e:
log_message(f"Thread {test_id}: Error saving text file: {str(e)}", debug, is_error=True) log_message(
f"Thread {test_id}: Error saving text file: {str(e)}", debug, is_error=True
)
# Make the TTS request # Make the TTS request
try: try:
@ -86,56 +96,102 @@ def request_tts(url, test_id, text, voice, output_dir, debug=False):
"model": "kokoro", "model": "kokoro",
"input": text, "input": text,
"voice": voice, "voice": voice,
"response_format": "wav" "response_format": "wav",
}, },
headers={"Accept": "audio/wav"}, headers={"Accept": "audio/wav"},
timeout=60 # Increase timeout to 60 seconds timeout=60, # Increase timeout to 60 seconds
) )
log_message(f"Thread {test_id}: Response status code: {response.status_code}", debug) log_message(
log_message(f"Thread {test_id}: Response content type: {response.headers.get('Content-Type', 'None')}", debug) f"Thread {test_id}: Response status code: {response.status_code}", debug
log_message(f"Thread {test_id}: Response content length: {len(response.content)} bytes", debug) )
log_message(
f"Thread {test_id}: Response content type: {response.headers.get('Content-Type', 'None')}",
debug,
)
log_message(
f"Thread {test_id}: Response content length: {len(response.content)} bytes",
debug,
)
if response.status_code != 200: if response.status_code != 200:
log_message(f"Thread {test_id}: API error: {response.status_code} - {response.text}", debug, is_error=True) log_message(
f"Thread {test_id}: API error: {response.status_code} - {response.text}",
debug,
is_error=True,
)
return False return False
# Check if we got valid audio data # Check if we got valid audio data
if len(response.content) < 100: # Sanity check - WAV files should be larger than this if (
log_message(f"Thread {test_id}: Received suspiciously small audio data: {len(response.content)} bytes", debug, is_error=True) len(response.content) < 100
log_message(f"Thread {test_id}: Content (base64): {base64.b64encode(response.content).decode('utf-8')}", debug, is_error=True) ): # Sanity check - WAV files should be larger than this
log_message(
f"Thread {test_id}: Received suspiciously small audio data: {len(response.content)} bytes",
debug,
is_error=True,
)
log_message(
f"Thread {test_id}: Content (base64): {base64.b64encode(response.content).decode('utf-8')}",
debug,
is_error=True,
)
return False return False
# Save the audio output with explicit error handling # Save the audio output with explicit error handling
try: try:
with open(output_file, "wb") as f: with open(output_file, "wb") as f:
bytes_written = f.write(response.content) bytes_written = f.write(response.content)
log_message(f"Thread {test_id}: Wrote {bytes_written} bytes to {output_file}", debug) log_message(
f"Thread {test_id}: Wrote {bytes_written} bytes to {output_file}",
debug,
)
# Verify the WAV file exists and has content # Verify the WAV file exists and has content
if os.path.exists(output_file): if os.path.exists(output_file):
file_size = os.path.getsize(output_file) file_size = os.path.getsize(output_file)
log_message(f"Thread {test_id}: Verified file exists with size: {file_size} bytes", debug) log_message(
f"Thread {test_id}: Verified file exists with size: {file_size} bytes",
debug,
)
# Validate WAV file by reading its headers # Validate WAV file by reading its headers
try: try:
with wave.open(output_file, 'rb') as wav_file: with wave.open(output_file, "rb") as wav_file:
channels = wav_file.getnchannels() channels = wav_file.getnchannels()
sample_width = wav_file.getsampwidth() sample_width = wav_file.getsampwidth()
framerate = wav_file.getframerate() framerate = wav_file.getframerate()
frames = wav_file.getnframes() frames = wav_file.getnframes()
log_message(f"Thread {test_id}: Valid WAV file - channels: {channels}, " log_message(
f"sample width: {sample_width}, framerate: {framerate}, frames: {frames}", debug) f"Thread {test_id}: Valid WAV file - channels: {channels}, "
f"sample width: {sample_width}, framerate: {framerate}, frames: {frames}",
debug,
)
except Exception as wav_error: except Exception as wav_error:
log_message(f"Thread {test_id}: Invalid WAV file: {str(wav_error)}", debug, is_error=True) log_message(
f"Thread {test_id}: Invalid WAV file: {str(wav_error)}",
debug,
is_error=True,
)
else: else:
log_message(f"Thread {test_id}: File was not created: {output_file}", debug, is_error=True) log_message(
f"Thread {test_id}: File was not created: {output_file}",
debug,
is_error=True,
)
except Exception as save_error: except Exception as save_error:
log_message(f"Thread {test_id}: Error saving audio file: {str(save_error)}", debug, is_error=True) log_message(
f"Thread {test_id}: Error saving audio file: {str(save_error)}",
debug,
is_error=True,
)
return False return False
end_time = time.time() end_time = time.time()
log_message(f"Thread {test_id}: Saved output to {output_file} (time: {end_time - start_time:.2f}s)", debug) log_message(
f"Thread {test_id}: Saved output to {output_file} (time: {end_time - start_time:.2f}s)",
debug,
)
return True return True
except requests.exceptions.Timeout: except requests.exceptions.Timeout:
@ -152,10 +208,16 @@ def worker_task(thread_id, args):
iteration = i + 1 iteration = i + 1
test_id = f"{thread_id:02d}_{iteration:02d}" test_id = f"{thread_id:02d}_{iteration:02d}"
text = generate_test_sentence(thread_id, iteration) text = generate_test_sentence(thread_id, iteration)
success = request_tts(args.url, test_id, text, args.voice, args.output_dir, args.debug) success = request_tts(
args.url, test_id, text, args.voice, args.output_dir, args.debug
)
if not success: if not success:
log_message(f"Thread {thread_id}: Iteration {iteration} failed", args.debug, is_error=True) log_message(
f"Thread {thread_id}: Iteration {iteration} failed",
args.debug,
is_error=True,
)
# Small delay between iterations to avoid overwhelming the API # Small delay between iterations to avoid overwhelming the API
time.sleep(0.1) time.sleep(0.1)
@ -172,9 +234,14 @@ def run_test(args):
with open(test_file, "w") as f: with open(test_file, "w") as f:
f.write("Testing write access\n") f.write("Testing write access\n")
os.remove(test_file) os.remove(test_file)
log_message(f"Successfully verified write access to output directory: {args.output_dir}") log_message(
f"Successfully verified write access to output directory: {args.output_dir}"
)
except Exception as e: except Exception as e:
log_message(f"Warning: Cannot write to output directory {args.output_dir}: {str(e)}", is_error=True) log_message(
f"Warning: Cannot write to output directory {args.output_dir}: {str(e)}",
is_error=True,
)
log_message(f"Current directory: {os.getcwd()}", is_error=True) log_message(f"Current directory: {os.getcwd()}", is_error=True)
log_message(f"Directory contents: {os.listdir('.')}", is_error=True) log_message(f"Directory contents: {os.listdir('.')}", is_error=True)
@ -184,13 +251,21 @@ def run_test(args):
if response.status_code == 200: if response.status_code == 200:
log_message(f"Successfully connected to Kokoro TTS service at {args.url}") log_message(f"Successfully connected to Kokoro TTS service at {args.url}")
else: else:
log_message(f"Warning: Kokoro TTS service health check returned status {response.status_code}", is_error=True) log_message(
f"Warning: Kokoro TTS service health check returned status {response.status_code}",
is_error=True,
)
except Exception as e: except Exception as e:
log_message(f"Warning: Cannot connect to Kokoro TTS service at {args.url}: {str(e)}", is_error=True) log_message(
f"Warning: Cannot connect to Kokoro TTS service at {args.url}: {str(e)}",
is_error=True,
)
# Record start time # Record start time
start_time = time.time() start_time = time.time()
log_message(f"Starting test with {args.threads} threads, {args.iterations} iterations per thread") log_message(
f"Starting test with {args.threads} threads, {args.iterations} iterations per thread"
)
# Create and start worker threads # Create and start worker threads
with concurrent.futures.ThreadPoolExecutor(max_workers=args.threads) as executor: with concurrent.futures.ThreadPoolExecutor(max_workers=args.threads) as executor:
@ -203,7 +278,9 @@ def run_test(args):
try: try:
future.result() future.result()
except Exception as e: except Exception as e:
log_message(f"Thread execution failed: {str(e)}", args.debug, is_error=True) log_message(
f"Thread execution failed: {str(e)}", args.debug, is_error=True
)
# Record end time and print summary # Record end time and print summary
end_time = time.time() end_time = time.time()
@ -214,8 +291,12 @@ def run_test(args):
log_message(f"Average time per request: {total_time / total_requests:.2f} seconds") log_message(f"Average time per request: {total_time / total_requests:.2f} seconds")
log_message(f"Requests per second: {total_requests / total_time:.2f}") log_message(f"Requests per second: {total_requests / total_time:.2f}")
log_message(f"Output files saved to: {os.path.abspath(args.output_dir)}") log_message(f"Output files saved to: {os.path.abspath(args.output_dir)}")
log_message("To verify, listen to the audio files and check if they match the text files") log_message(
log_message("If you hear audio describing a different test number than the filename, you've found a race condition") "To verify, listen to the audio files and check if they match the text files"
)
log_message(
"If you hear audio describing a different test number than the filename, you've found a race condition"
)
def analyze_audio_files(output_dir): def analyze_audio_files(output_dir):
@ -227,30 +308,34 @@ def analyze_audio_files(output_dir):
log_message(f"Found {len(wav_files)} WAV files and {len(txt_files)} TXT files") log_message(f"Found {len(wav_files)} WAV files and {len(txt_files)} TXT files")
if len(wav_files) == 0: if len(wav_files) == 0:
log_message("No WAV files found! This indicates the TTS service requests may be failing.", is_error=True) log_message(
log_message("Check the connection to the TTS service and the response status codes above.", is_error=True) "No WAV files found! This indicates the TTS service requests may be failing.",
is_error=True,
)
log_message(
"Check the connection to the TTS service and the response status codes above.",
is_error=True,
)
file_stats = [] file_stats = []
for wav_path in wav_files: for wav_path in wav_files:
try: try:
with wave.open(str(wav_path), 'rb') as wav_file: with wave.open(str(wav_path), "rb") as wav_file:
frames = wav_file.getnframes() frames = wav_file.getnframes()
rate = wav_file.getframerate() rate = wav_file.getframerate()
duration = frames / rate duration = frames / rate
# Get corresponding text # Get corresponding text
text_path = wav_path.with_suffix('.txt') text_path = wav_path.with_suffix(".txt")
if text_path.exists(): if text_path.exists():
with open(text_path, 'r') as text_file: with open(text_path, "r") as text_file:
text = text_file.read().strip() text = text_file.read().strip()
else: else:
text = "N/A" text = "N/A"
file_stats.append({ file_stats.append(
'filename': wav_path.name, {"filename": wav_path.name, "duration": duration, "text": text}
'duration': duration, )
'text': text
})
except Exception as e: except Exception as e:
log_message(f"Error analyzing {wav_path}: {str(e)}", False, is_error=True) log_message(f"Error analyzing {wav_path}: {str(e)}", False, is_error=True)
@ -260,12 +345,17 @@ def analyze_audio_files(output_dir):
log_message(f"{'Filename':<20}{'Duration':<12}{'Text':<60}") log_message(f"{'Filename':<20}{'Duration':<12}{'Text':<60}")
log_message("-" * 92) log_message("-" * 92)
for stat in file_stats: for stat in file_stats:
log_message(f"{stat['filename']:<20}{stat['duration']:<12.2f}{stat['text'][:57]+'...' if len(stat['text']) > 60 else stat['text']:<60}") log_message(
f"{stat['filename']:<20}{stat['duration']:<12.2f}{stat['text'][:57] + '...' if len(stat['text']) > 60 else stat['text']:<60}"
)
# List missing WAV files where text files exist # List missing WAV files where text files exist
missing_wavs = set(p.stem for p in txt_files) - set(p.stem for p in wav_files) missing_wavs = set(p.stem for p in txt_files) - set(p.stem for p in wav_files)
if missing_wavs: if missing_wavs:
log_message(f"\nFound {len(missing_wavs)} text files without corresponding WAV files:", is_error=True) log_message(
f"\nFound {len(missing_wavs)} text files without corresponding WAV files:",
is_error=True,
)
for stem in sorted(list(missing_wavs))[:10]: # Limit to 10 for readability for stem in sorted(list(missing_wavs))[:10]: # Limit to 10 for readability
log_message(f" - {stem}.txt (no WAV file)", is_error=True) log_message(f" - {stem}.txt (no WAV file)", is_error=True)
if len(missing_wavs) > 10: if len(missing_wavs) > 10:
@ -280,5 +370,9 @@ if __name__ == "__main__":
log_message("\nNext Steps:") log_message("\nNext Steps:")
log_message("1. Listen to the generated audio files") log_message("1. Listen to the generated audio files")
log_message("2. Verify if each audio correctly says its ID number") log_message("2. Verify if each audio correctly says its ID number")
log_message("3. Check for any mismatches between the audio content and the text files") log_message(
log_message("4. If mismatches are found, you've successfully reproduced the race condition") "3. Check for any mismatches between the audio content and the text files"
)
log_message(
"4. If mismatches are found, you've successfully reproduced the race condition"
)

26
dev/Test copy.py
View file

@ -4,7 +4,7 @@ import json
import pydub import pydub
import requests import requests
text="""Delving into the Abyss: A Deeper Exploration of Meaning in 5 Seconds of Summer's "Jet Black Heart" text = """Delving into the Abyss: A Deeper Exploration of Meaning in 5 Seconds of Summer's "Jet Black Heart"
5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos. Through potent metaphors, starkly honest lyrics, and a sonic landscape that mirrors its thematic weight, the song offers a profound meditation on the human condition, grappling with the shadows that reside within us all and their far-reaching consequences. 5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos. Through potent metaphors, starkly honest lyrics, and a sonic landscape that mirrors its thematic weight, the song offers a profound meditation on the human condition, grappling with the shadows that reside within us all and their far-reaching consequences.
@ -25,7 +25,7 @@ In conclusion, "Jet Black Heart" by 5 Seconds of Summer is far more than a typic
5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos.""" 5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos."""
Type="wav" Type = "wav"
response = requests.post( response = requests.post(
"http://localhost:8880/dev/captioned_speech", "http://localhost:8880/dev/captioned_speech",
json={ json={
@ -36,30 +36,34 @@ response = requests.post(
"response_format": Type, "response_format": Type,
"stream": True, "stream": True,
}, },
stream=True stream=True,
) )
f=open(f"outputstream.{Type}","wb") f = open(f"outputstream.{Type}", "wb")
for chunk in response.iter_lines(decode_unicode=True): for chunk in response.iter_lines(decode_unicode=True):
if chunk: if chunk:
temp_json=json.loads(chunk) temp_json = json.loads(chunk)
if temp_json["timestamps"] != []: if temp_json["timestamps"] != []:
chunk_json=temp_json chunk_json = temp_json
# Decode base 64 stream to bytes # Decode base 64 stream to bytes
chunk_audio=base64.b64decode(temp_json["audio"].encode("utf-8")) chunk_audio = base64.b64decode(temp_json["audio"].encode("utf-8"))
# Process streaming chunks # Process streaming chunks
f.write(chunk_audio) f.write(chunk_audio)
# Print word level timestamps # Print word level timestamps
last_chunks={"start_time":chunk_json["timestamps"][-10]["start_time"],"end_time":chunk_json["timestamps"][-3]["end_time"],"word":" ".join([X["word"] for X in chunk_json["timestamps"][-10:-3]])} last_chunks = {
"start_time": chunk_json["timestamps"][-10]["start_time"],
"end_time": chunk_json["timestamps"][-3]["end_time"],
"word": " ".join([X["word"] for X in chunk_json["timestamps"][-10:-3]]),
}
print(f"CUTTING TO {last_chunks['word']}") print(f"CUTTING TO {last_chunks['word']}")
audioseg=pydub.AudioSegment.from_file(f"outputstream.{Type}",format=Type) audioseg = pydub.AudioSegment.from_file(f"outputstream.{Type}", format=Type)
audioseg=audioseg[last_chunks["start_time"]*1000:last_chunks["end_time"] * 1000] audioseg = audioseg[last_chunks["start_time"] * 1000 : last_chunks["end_time"] * 1000]
audioseg.export(f"outputstreamcut.{Type}",format=Type) audioseg.export(f"outputstreamcut.{Type}", format=Type)
""" """

6
dev/Test money.py
View file

@ -3,12 +3,12 @@ import json
import requests import requests
text="""the administration has offered up a platter of repression for more than a year and is still slated to lose $400 million. text = """the administration has offered up a platter of repression for more than a year and is still slated to lose $400 million.
Columbia is the largest private landowner in New York City and boasts an endowment of $14.8 billion;""" Columbia is the largest private landowner in New York City and boasts an endowment of $14.8 billion;"""
Type="wav" Type = "wav"
response = requests.post( response = requests.post(
"http://localhost:8880/v1/audio/speech", "http://localhost:8880/v1/audio/speech",
@ -20,7 +20,7 @@ response = requests.post(
"response_format": Type, "response_format": Type,
"stream": False, "stream": False,
}, },
stream=True stream=True,
) )
with open(f"outputnostreammoney.{Type}", "wb") as f: with open(f"outputnostreammoney.{Type}", "wb") as f:

1
dev/Test num.py
View file

@ -12,6 +12,7 @@ def conditional_int(number: float, threshold: float = 0.00001):
return int(round(number)) return int(round(number))
return number return number
def handle_money(m: re.Match[str]) -> str: def handle_money(m: re.Match[str]) -> str:
"""Convert money expressions to spoken form""" """Convert money expressions to spoken form"""

12
dev/Test.py
View file

@ -3,7 +3,7 @@ import json
import requests import requests
text="""Delving into the Abyss: A Deeper Exploration of Meaning in 5 Seconds of Summer's "Jet Black Heart" text = """Delving into the Abyss: A Deeper Exploration of Meaning in 5 Seconds of Summer's "Jet Black Heart"
5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos. Through potent metaphors, starkly honest lyrics, and a sonic landscape that mirrors its thematic weight, the song offers a profound meditation on the human condition, grappling with the shadows that reside within us all and their far-reaching consequences. 5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos. Through potent metaphors, starkly honest lyrics, and a sonic landscape that mirrors its thematic weight, the song offers a profound meditation on the human condition, grappling with the shadows that reside within us all and their far-reaching consequences.
@ -19,12 +19,12 @@ Beyond the lyrical content, the musical elements of "Jet Black Heart" contribute
In conclusion, "Jet Black Heart" by 5 Seconds of Summer is far more than a typical pop song; it is a poignant and deeply resonant exploration of inner darkness, self-destructive tendencies, and the fragile yet persistent hope for human connection and redemption. Through its powerful central metaphor of the "jet black heart," its unflinching portrayal of internal turmoil, and its subtle yet potent message of vulnerability and potential transformation, the song resonates with anyone who has grappled with their own inner demons and the complexities of human relationships. It is a reminder that even in the deepest darkness, a flicker of hope can endure, and that true healing and connection often emerge from the courageous act of confronting and sharing our most vulnerable selves. "Jet Black Heart" stands as a testament to 5 Seconds of Summer's artistic growth, showcasing their capacity to delve into profound emotional territories and create music that is not only catchy and engaging but also deeply meaningful and emotionally resonant, solidifying their position as a band capable of capturing the complexities of the human experience.""" In conclusion, "Jet Black Heart" by 5 Seconds of Summer is far more than a typical pop song; it is a poignant and deeply resonant exploration of inner darkness, self-destructive tendencies, and the fragile yet persistent hope for human connection and redemption. Through its powerful central metaphor of the "jet black heart," its unflinching portrayal of internal turmoil, and its subtle yet potent message of vulnerability and potential transformation, the song resonates with anyone who has grappled with their own inner demons and the complexities of human relationships. It is a reminder that even in the deepest darkness, a flicker of hope can endure, and that true healing and connection often emerge from the courageous act of confronting and sharing our most vulnerable selves. "Jet Black Heart" stands as a testament to 5 Seconds of Summer's artistic growth, showcasing their capacity to delve into profound emotional territories and create music that is not only catchy and engaging but also deeply meaningful and emotionally resonant, solidifying their position as a band capable of capturing the complexities of the human experience."""
text="""Delving into the Abyss: A Deeper Exploration of Meaning in 5 Seconds of Summer's "Jet Black Heart" text = """Delving into the Abyss: A Deeper Exploration of Meaning in 5 Seconds of Summer's "Jet Black Heart"
5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos.""" 5 Seconds of Summer, initially perceived as purveyors of upbeat, radio-friendly pop-punk, embarked on a significant artistic evolution with their album Sounds Good Feels Good. Among its tracks, "Jet Black Heart" stands out as a powerful testament to this shift, moving beyond catchy melodies and embracing a darker, more emotionally complex sound. Released in 2015, the song transcends the typical themes of youthful exuberance and romantic angst, instead plunging into the depths of personal turmoil and the corrosive effects of inner darkness on interpersonal relationships. "Jet Black Heart" is not merely a song about heartbreak; it is a raw and vulnerable exploration of internal struggle, self-destructive patterns, and the precarious flicker of hope that persists even in the face of profound emotional chaos."""
Type="wav" Type = "wav"
response = requests.post( response = requests.post(
@ -37,11 +37,11 @@ response = requests.post(
"response_format": Type, "response_format": Type,
"stream": True, "stream": True,
}, },
stream=True stream=True,
) )
f=open(f"outputstream.{Type}","wb") f = open(f"outputstream.{Type}", "wb")
for chunk in response.iter_content(): for chunk in response.iter_content():
if chunk: if chunk:
# Process streaming chunks # Process streaming chunks
@ -57,7 +57,7 @@ response = requests.post(
"response_format": Type, "response_format": Type,
"stream": False, "stream": False,
}, },
stream=True stream=True,
) )
with open(f"outputnostream.{Type}", "wb") as f: with open(f"outputnostream.{Type}", "wb") as f:

4
docker/scripts/download_model.py
View file

@ -91,9 +91,7 @@ def main():
parser = argparse.ArgumentParser(description="Download Kokoro v1.0 model") parser = argparse.ArgumentParser(description="Download Kokoro v1.0 model")
parser.add_argument( parser.add_argument(
"--output", "--output", required=True, help="Output directory for model files"
required=True,
help="Output directory for model files"
) )
args = parser.parse_args() args = parser.parse_args()

7
scripts/fix_misaki.py
View file

@ -9,6 +9,7 @@ import sys
# Find the misaki package # Find the misaki package
try: try:
import misaki import misaki
misaki_path = os.path.dirname(misaki.__file__) misaki_path = os.path.dirname(misaki.__file__)
print(f"Found misaki package at: {misaki_path}") print(f"Found misaki package at: {misaki_path}")
except ImportError: except ImportError:
@ -23,7 +24,7 @@ if not os.path.exists(espeak_file):
sys.exit(1) sys.exit(1)
# Read the current content # Read the current content
with open(espeak_file, 'r') as f: with open(espeak_file, "r") as f:
content = f.read() content = f.read()
# Check if the problematic line exists # Check if the problematic line exists
@ -32,11 +33,11 @@ if "EspeakWrapper.set_data_path(espeakng_loader.get_data_path())" in content:
new_content = content.replace( new_content = content.replace(
"EspeakWrapper.set_data_path(espeakng_loader.get_data_path())", "EspeakWrapper.set_data_path(espeakng_loader.get_data_path())",
"# Fixed line to use data_path attribute instead of set_data_path method\n" "# Fixed line to use data_path attribute instead of set_data_path method\n"
"EspeakWrapper.data_path = espeakng_loader.get_data_path()" "EspeakWrapper.data_path = espeakng_loader.get_data_path()",
) )
# Write the modified content back # Write the modified content back
with open(espeak_file, 'w') as f: with open(espeak_file, "w") as f:
f.write(new_content) f.write(new_content)
print(f"Successfully patched {espeak_file}") print(f"Successfully patched {espeak_file}")

34
scripts/update_badges.py
View file

@ -39,15 +39,13 @@ def extract_dependency_info():
return info return info
def run_pytest_with_coverage(): def run_pytest_with_coverage():
"""Run pytest with coverage and return the results""" """Run pytest with coverage and return the results"""
try: try:
# Run pytest with coverage # Run pytest with coverage
result = subprocess.run( result = subprocess.run(
["pytest", "--cov=api", "-v"], ["pytest", "--cov=api", "-v"], capture_output=True, text=True, check=True
capture_output=True,
text=True,
check=True
) )
# Extract test results # Extract test results
@ -56,10 +54,7 @@ def run_pytest_with_coverage():
# Extract coverage from .coverage file # Extract coverage from .coverage file
coverage_output = subprocess.run( coverage_output = subprocess.run(
["coverage", "report"], ["coverage", "report"], capture_output=True, text=True, check=True
capture_output=True,
text=True,
check=True
).stdout ).stdout
# Extract total coverage percentage # Extract total coverage percentage
@ -72,6 +67,7 @@ def run_pytest_with_coverage():
print(f"Output: {e.output}") print(f"Output: {e.output}")
return 0, "0" return 0, "0"
def update_readme_badges(passed_tests, coverage_percentage, dep_info): def update_readme_badges(passed_tests, coverage_percentage, dep_info):
"""Update the badges in the README file""" """Update the badges in the README file"""
readme_path = Path("README.md") readme_path = Path("README.md")
@ -83,16 +79,16 @@ def update_readme_badges(passed_tests, coverage_percentage, dep_info):
# Update tests badge # Update tests badge
content = re.sub( content = re.sub(
r'!\[Tests\]\(https://img\.shields\.io/badge/tests-\d+%20passed-[a-zA-Z]+\)', r"!\[Tests\]\(https://img\.shields\.io/badge/tests-\d+%20passed-[a-zA-Z]+\)",
f'![Tests](https://img.shields.io/badge/tests-{passed_tests}%20passed-darkgreen)', f"![Tests](https://img.shields.io/badge/tests-{passed_tests}%20passed-darkgreen)",
content content,
) )
# Update coverage badge # Update coverage badge
content = re.sub( content = re.sub(
r'!\[Coverage\]\(https://img\.shields\.io/badge/coverage-\d+%25-[a-zA-Z]+\)', r"!\[Coverage\]\(https://img\.shields\.io/badge/coverage-\d+%25-[a-zA-Z]+\)",
f'![Coverage](https://img.shields.io/badge/coverage-{coverage_percentage}%25-tan)', f"![Coverage](https://img.shields.io/badge/coverage-{coverage_percentage}%25-tan)",
content content,
) )
# Update kokoro badge # Update kokoro badge
@ -100,9 +96,9 @@ def update_readme_badges(passed_tests, coverage_percentage, dep_info):
# Find badge like kokoro-v0.9.2::abcdefg-BB5420 or kokoro-v0.9.2-BB5420 # Find badge like kokoro-v0.9.2::abcdefg-BB5420 or kokoro-v0.9.2-BB5420
kokoro_version = dep_info["kokoro"]["version"] kokoro_version = dep_info["kokoro"]["version"]
content = re.sub( content = re.sub(
r'(!\[Kokoro\]\(https://img\.shields\.io/badge/kokoro-)[^)-]+(-BB5420\))', r"(!\[Kokoro\]\(https://img\.shields\.io/badge/kokoro-)[^)-]+(-BB5420\))",
lambda m: f"{m.group(1)}{kokoro_version}{m.group(2)}", lambda m: f"{m.group(1)}{kokoro_version}{m.group(2)}",
content content,
) )
# Update misaki badge # Update misaki badge
@ -110,14 +106,15 @@ def update_readme_badges(passed_tests, coverage_percentage, dep_info):
# Find badge like misaki-v0.9.3::abcdefg-B8860B or misaki-v0.9.3-B8860B # Find badge like misaki-v0.9.3::abcdefg-B8860B or misaki-v0.9.3-B8860B
misaki_version = dep_info["misaki"]["version"] misaki_version = dep_info["misaki"]["version"]
content = re.sub( content = re.sub(
r'(!\[Misaki\]\(https://img\.shields\.io/badge/misaki-)[^)-]+(-B8860B\))', r"(!\[Misaki\]\(https://img\.shields\.io/badge/misaki-)[^)-]+(-B8860B\))",
lambda m: f"{m.group(1)}{misaki_version}{m.group(2)}", lambda m: f"{m.group(1)}{misaki_version}{m.group(2)}",
content content,
) )
readme_path.write_text(content) readme_path.write_text(content)
return True return True
def main(): def main():
# Get dependency info # Get dependency info
dep_info = extract_dependency_info() dep_info = extract_dependency_info()
@ -137,5 +134,6 @@ def main():
else: else:
print("Failed to update badges") print("Failed to update badges")
if __name__ == "__main__": if __name__ == "__main__":
main() main()

128
scripts/update_version.py
View file

@ -21,6 +21,7 @@ HELM_CHART_FILE = ROOT_DIR / "charts" / "kokoro-fastapi" / "Chart.yaml"
README_FILE = ROOT_DIR / "README.md" README_FILE = ROOT_DIR / "README.md"
# --- End Configuration --- # --- End Configuration ---
def update_pyproject(version: str): def update_pyproject(version: str):
"""Updates the version in pyproject.toml""" """Updates the version in pyproject.toml"""
if not PYPROJECT_FILE.exists(): if not PYPROJECT_FILE.exists():
@ -42,13 +43,16 @@ def update_pyproject(version: str):
print(f"Already up-to-date: {PYPROJECT_FILE} (version {version})") print(f"Already up-to-date: {PYPROJECT_FILE} (version {version})")
else: else:
# Perform replacement # Perform replacement
new_content = re.sub(pattern, rf'\1"{version}"', content, count=1, flags=re.MULTILINE) new_content = re.sub(
pattern, rf'\1"{version}"', content, count=1, flags=re.MULTILINE
)
PYPROJECT_FILE.write_text(new_content) PYPROJECT_FILE.write_text(new_content)
print(f"Updated {PYPROJECT_FILE} from {current_version} to {version}") print(f"Updated {PYPROJECT_FILE} from {current_version} to {version}")
except Exception as e: except Exception as e:
print(f"Error processing {PYPROJECT_FILE}: {e}") print(f"Error processing {PYPROJECT_FILE}: {e}")
def update_helm_chart(version: str): def update_helm_chart(version: str):
"""Updates the version and appVersion in the Helm chart""" """Updates the version and appVersion in the Helm chart"""
if not HELM_CHART_FILE.exists(): if not HELM_CHART_FILE.exists():
@ -65,65 +69,92 @@ def update_helm_chart(version: str):
version_pattern = r"^(version:\s*)(\S+)" version_pattern = r"^(version:\s*)(\S+)"
current_version_match = re.search(version_pattern, content, flags=re.MULTILINE) current_version_match = re.search(version_pattern, content, flags=re.MULTILINE)
if current_version_match and current_version_match.group(2) != version: if current_version_match and current_version_match.group(2) != version:
content = re.sub(version_pattern, rf"\g<1>{version}", content, count=1, flags=re.MULTILINE) content = re.sub(
print(f"Updating 'version' in {HELM_CHART_FILE} from {current_version_match.group(2)} to {version}") version_pattern,
rf"\g<1>{version}",
content,
count=1,
flags=re.MULTILINE,
)
print(
f"Updating 'version' in {HELM_CHART_FILE} from {current_version_match.group(2)} to {version}"
)
updated_count += 1 updated_count += 1
elif current_version_match: elif current_version_match:
print(f"Already up-to-date: 'version' in {HELM_CHART_FILE} is {version}") print(f"Already up-to-date: 'version' in {HELM_CHART_FILE} is {version}")
else: else:
print(f"Warning: 'version:' pattern not found in {HELM_CHART_FILE}") print(f"Warning: 'version:' pattern not found in {HELM_CHART_FILE}")
# Update 'appVersion:' line (quoted or unquoted) # Update 'appVersion:' line (quoted or unquoted)
# Looks for 'appVersion:' followed by optional whitespace, optional quote, the version, optional quote # Looks for 'appVersion:' followed by optional whitespace, optional quote, the version, optional quote
app_version_pattern = r"^(appVersion:\s*)(\"?)([^\"\s]+)(\"?)" app_version_pattern = r"^(appVersion:\s*)(\"?)([^\"\s]+)(\"?)"
current_app_version_match = re.search(app_version_pattern, content, flags=re.MULTILINE) current_app_version_match = re.search(
app_version_pattern, content, flags=re.MULTILINE
)
if current_app_version_match: if current_app_version_match:
leading_whitespace = current_app_version_match.group(1) # e.g., "appVersion: " leading_whitespace = current_app_version_match.group(
opening_quote = current_app_version_match.group(2) # e.g., '"' or '' 1
current_app_ver = current_app_version_match.group(3) # e.g., '0.2.0' ) # e.g., "appVersion: "
closing_quote = current_app_version_match.group(4) # e.g., '"' or '' opening_quote = current_app_version_match.group(2) # e.g., '"' or ''
current_app_ver = current_app_version_match.group(3) # e.g., '0.2.0'
closing_quote = current_app_version_match.group(4) # e.g., '"' or ''
# Check if quotes were consistent (both present or both absent) # Check if quotes were consistent (both present or both absent)
if opening_quote != closing_quote: if opening_quote != closing_quote:
print(f"Warning: Inconsistent quotes found for appVersion in {HELM_CHART_FILE}. Skipping update for this line.") print(
elif current_app_ver == version and opening_quote == '"': # Check if already correct *and* quoted f"Warning: Inconsistent quotes found for appVersion in {HELM_CHART_FILE}. Skipping update for this line."
print(f"Already up-to-date: 'appVersion' in {HELM_CHART_FILE} is \"{version}\"") )
elif (
current_app_ver == version and opening_quote == '"'
): # Check if already correct *and* quoted
print(
f"Already up-to-date: 'appVersion' in {HELM_CHART_FILE} is \"{version}\""
)
else: else:
# Always replace with the quoted version # Always replace with the quoted version
replacement = f'{leading_whitespace}"{version}"' # Ensure quotes replacement = f'{leading_whitespace}"{version}"' # Ensure quotes
original_display = f"{opening_quote}{current_app_ver}{closing_quote}" # How it looked before original_display = f"{opening_quote}{current_app_ver}{closing_quote}" # How it looked before
target_display = f'"{version}"' # How it should look target_display = f'"{version}"' # How it should look
# Only report update if the displayed value actually changes
if original_display != target_display:
content = re.sub(app_version_pattern, replacement, content, count=1, flags=re.MULTILINE)
print(f"Updating 'appVersion' in {HELM_CHART_FILE} from {original_display} to {target_display}")
updated_count += 1
else:
# It matches the target version but might need quoting fixed silently if we didn't update
# Or it was already correct. Check if content changed. If not, report up-to-date.
if not (content != original_content and updated_count > 0): # Avoid double message if version also changed
print(f"Already up-to-date: 'appVersion' in {HELM_CHART_FILE} is {target_display}")
# Only report update if the displayed value actually changes
if original_display != target_display:
content = re.sub(
app_version_pattern,
replacement,
content,
count=1,
flags=re.MULTILINE,
)
print(
f"Updating 'appVersion' in {HELM_CHART_FILE} from {original_display} to {target_display}"
)
updated_count += 1
else:
# It matches the target version but might need quoting fixed silently if we didn't update
# Or it was already correct. Check if content changed. If not, report up-to-date.
if not (
content != original_content and updated_count > 0
): # Avoid double message if version also changed
print(
f"Already up-to-date: 'appVersion' in {HELM_CHART_FILE} is {target_display}"
)
else: else:
print(f"Warning: 'appVersion:' pattern not found in {HELM_CHART_FILE}") print(f"Warning: 'appVersion:' pattern not found in {HELM_CHART_FILE}")
# Write back only if changes were made # Write back only if changes were made
if content != original_content: if content != original_content:
HELM_CHART_FILE.write_text(content) HELM_CHART_FILE.write_text(content)
# Confirmation message printed above during the specific update # Confirmation message printed above during the specific update
elif updated_count == 0 and current_version_match and current_app_version_match : elif updated_count == 0 and current_version_match and current_app_version_match:
# If no updates were made but patterns were found, confirm it's up-to-date overall # If no updates were made but patterns were found, confirm it's up-to-date overall
print(f"Already up-to-date: {HELM_CHART_FILE} (version {version})") print(f"Already up-to-date: {HELM_CHART_FILE} (version {version})")
except Exception as e: except Exception as e:
print(f"Error processing {HELM_CHART_FILE}: {e}") print(f"Error processing {HELM_CHART_FILE}: {e}")
def update_readme(version_with_v: str): def update_readme(version_with_v: str):
"""Updates Docker image tags in README.md""" """Updates Docker image tags in README.md"""
if not README_FILE.exists(): if not README_FILE.exists():
@ -133,30 +164,34 @@ def update_readme(version_with_v: str):
try: try:
content = README_FILE.read_text() content = README_FILE.read_text()
# Regex to find and capture current ghcr.io/.../kokoro-fastapi-(cpu|gpu):vX.Y.Z # Regex to find and capture current ghcr.io/.../kokoro-fastapi-(cpu|gpu):vX.Y.Z
pattern = r'(ghcr\.io/remsky/kokoro-fastapi-(?:cpu|gpu)):(v\d+\.\d+\.\d+)' pattern = r"(ghcr\.io/remsky/kokoro-fastapi-(?:cpu|gpu)):(v\d+\.\d+\.\d+)"
matches = list(re.finditer(pattern, content)) # Find all occurrences matches = list(re.finditer(pattern, content)) # Find all occurrences
if not matches: if not matches:
print(f"Warning: Docker image tag pattern not found in {README_FILE}") print(f"Warning: Docker image tag pattern not found in {README_FILE}")
else: else:
updated_needed = False updated_needed = False
for match in matches: for match in matches:
current_tag = match.group(2) current_tag = match.group(2)
if current_tag != version_with_v: if current_tag != version_with_v:
updated_needed = True updated_needed = True
break # Only need one mismatch to trigger update break # Only need one mismatch to trigger update
if updated_needed: if updated_needed:
# Perform replacement on all occurrences # Perform replacement on all occurrences
new_content = re.sub(pattern, rf'\1:{version_with_v}', content) new_content = re.sub(pattern, rf"\1:{version_with_v}", content)
README_FILE.write_text(new_content) README_FILE.write_text(new_content)
print(f"Updated Docker image tags in {README_FILE} to {version_with_v}") print(f"Updated Docker image tags in {README_FILE} to {version_with_v}")
else: else:
print(f"Already up-to-date: Docker image tags in {README_FILE} (version {version_with_v})") print(
f"Already up-to-date: Docker image tags in {README_FILE} (version {version_with_v})"
)
# Check for ':latest' tag usage remains the same # Check for ':latest' tag usage remains the same
if ':latest' in content: if ":latest" in content:
print(f"Warning: Found ':latest' tag in {README_FILE}. Consider updating manually if needed.") print(
f"Warning: Found ':latest' tag in {README_FILE}. Consider updating manually if needed."
)
except Exception as e: except Exception as e:
print(f"Error processing {README_FILE}: {e}") print(f"Error processing {README_FILE}: {e}")
@ -171,8 +206,10 @@ def main():
try: try:
version = VERSION_FILE.read_text().strip() version = VERSION_FILE.read_text().strip()
if not re.match(r"^\d+\.\d+\.\d+$", version): if not re.match(r"^\d+\.\d+\.\d+$", version):
print(f"Error: Invalid version format '{version}' in {VERSION_FILE}. Expected X.Y.Z") print(
return f"Error: Invalid version format '{version}' in {VERSION_FILE}. Expected X.Y.Z"
)
return
except Exception as e: except Exception as e:
print(f"Error reading {VERSION_FILE}: {e}") print(f"Error reading {VERSION_FILE}: {e}")
return return
@ -192,5 +229,6 @@ def main():
print("-" * 20) print("-" * 20)
print("Version update script finished.") print("Version update script finished.")
if __name__ == "__main__": if __name__ == "__main__":
main() main()

9
ui/depr_tests/conftest.py
View file

@ -31,8 +31,11 @@ async def mock_tts_service(mock_model_manager, mock_voice_manager):
@pytest.fixture(autouse=True) @pytest.fixture(autouse=True)
async def setup_mocks(monkeypatch, mock_model_manager, mock_voice_manager, mock_tts_service): async def setup_mocks(
monkeypatch, mock_model_manager, mock_voice_manager, mock_tts_service
):
"""Setup global mocks for UI tests""" """Setup global mocks for UI tests"""
async def mock_get_model(): async def mock_get_model():
return mock_model_manager return mock_model_manager
@ -44,4 +47,6 @@ async def setup_mocks(monkeypatch, mock_model_manager, mock_voice_manager, mock_
monkeypatch.setattr("api.src.inference.model_manager.get_manager", mock_get_model) monkeypatch.setattr("api.src.inference.model_manager.get_manager", mock_get_model)
monkeypatch.setattr("api.src.inference.voice_manager.get_manager", mock_get_voice) monkeypatch.setattr("api.src.inference.voice_manager.get_manager", mock_get_voice)
monkeypatch.setattr("api.src.services.tts_service.TTSService.create", mock_create_service) monkeypatch.setattr(
"api.src.services.tts_service.TTSService.create", mock_create_service
)

28
ui/depr_tests/test_api.py
View file

@ -59,9 +59,11 @@ def test_check_api_status_connection_error():
def test_text_to_speech_success(mock_response, tmp_path): def test_text_to_speech_success(mock_response, tmp_path):
"""Test successful speech generation""" """Test successful speech generation"""
with patch("requests.post", return_value=mock_response({})), patch( with (
"ui.lib.api.OUTPUTS_DIR", str(tmp_path) patch("requests.post", return_value=mock_response({})),
), patch("builtins.open", mock_open()) as mock_file: patch("ui.lib.api.OUTPUTS_DIR", str(tmp_path)),
patch("builtins.open", mock_open()) as mock_file,
):
result = api.text_to_speech("test text", "voice1", "mp3", 1.0) result = api.text_to_speech("test text", "voice1", "mp3", 1.0)
assert result is not None assert result is not None
@ -116,9 +118,11 @@ def test_text_to_speech_api_params(mock_response, tmp_path):
] ]
for input_voice, expected_voice in test_cases: for input_voice, expected_voice in test_cases:
with patch("requests.post") as mock_post, patch( with (
"ui.lib.api.OUTPUTS_DIR", str(tmp_path) patch("requests.post") as mock_post,
), patch("builtins.open", mock_open()): patch("ui.lib.api.OUTPUTS_DIR", str(tmp_path)),
patch("builtins.open", mock_open()),
):
mock_post.return_value = mock_response({}) mock_post.return_value = mock_response({})
api.text_to_speech("test text", input_voice, "mp3", 1.5) api.text_to_speech("test text", input_voice, "mp3", 1.5)
@ -149,11 +153,15 @@ def test_text_to_speech_output_filename(mock_response, tmp_path):
] ]
for input_voice, filename_check in test_cases: for input_voice, filename_check in test_cases:
with patch("requests.post", return_value=mock_response({})), patch( with (
"ui.lib.api.OUTPUTS_DIR", str(tmp_path) patch("requests.post", return_value=mock_response({})),
), patch("builtins.open", mock_open()) as mock_file: patch("ui.lib.api.OUTPUTS_DIR", str(tmp_path)),
patch("builtins.open", mock_open()) as mock_file,
):
result = api.text_to_speech("test text", input_voice, "mp3", 1.0) result = api.text_to_speech("test text", input_voice, "mp3", 1.0)
assert result is not None assert result is not None
assert filename_check(result), f"Expected voice pattern not found in filename: {result}" assert filename_check(result), (
f"Expected voice pattern not found in filename: {result}"
)
mock_file.assert_called_once() mock_file.assert_called_once()

5
ui/depr_tests/test_files.py
View file

@ -15,8 +15,9 @@ def mock_dirs(tmp_path):
inputs_dir.mkdir() inputs_dir.mkdir()
outputs_dir.mkdir() outputs_dir.mkdir()
with patch("ui.lib.files.INPUTS_DIR", str(inputs_dir)), patch( with (
"ui.lib.files.OUTPUTS_DIR", str(outputs_dir) patch("ui.lib.files.INPUTS_DIR", str(inputs_dir)),
patch("ui.lib.files.OUTPUTS_DIR", str(outputs_dir)),
): ):
yield inputs_dir, outputs_dir yield inputs_dir, outputs_dir

22
ui/depr_tests/test_interface.py
View file

@ -62,8 +62,9 @@ def test_interface_html_links():
def test_update_status_available(mock_timer): def test_update_status_available(mock_timer):
"""Test status update when service is available""" """Test status update when service is available"""
voices = ["voice1", "voice2"] voices = ["voice1", "voice2"]
with patch("ui.lib.api.check_api_status", return_value=(True, voices)), patch( with (
"gradio.Timer", return_value=mock_timer patch("ui.lib.api.check_api_status", return_value=(True, voices)),
patch("gradio.Timer", return_value=mock_timer),
): ):
demo = create_interface() demo = create_interface()
@ -81,8 +82,9 @@ def test_update_status_available(mock_timer):
def test_update_status_unavailable(mock_timer): def test_update_status_unavailable(mock_timer):
"""Test status update when service is unavailable""" """Test status update when service is unavailable"""
with patch("ui.lib.api.check_api_status", return_value=(False, [])), patch( with (
"gradio.Timer", return_value=mock_timer patch("ui.lib.api.check_api_status", return_value=(False, [])),
patch("gradio.Timer", return_value=mock_timer),
): ):
demo = create_interface() demo = create_interface()
update_fn = mock_timer.events[0].fn update_fn = mock_timer.events[0].fn
@ -97,9 +99,10 @@ def test_update_status_unavailable(mock_timer):
def test_update_status_error(mock_timer): def test_update_status_error(mock_timer):
"""Test status update when an error occurs""" """Test status update when an error occurs"""
with patch( with (
"ui.lib.api.check_api_status", side_effect=Exception("Test error") patch("ui.lib.api.check_api_status", side_effect=Exception("Test error")),
), patch("gradio.Timer", return_value=mock_timer): patch("gradio.Timer", return_value=mock_timer),
):
demo = create_interface() demo = create_interface()
update_fn = mock_timer.events[0].fn update_fn = mock_timer.events[0].fn
@ -113,8 +116,9 @@ def test_update_status_error(mock_timer):
def test_timer_configuration(mock_timer): def test_timer_configuration(mock_timer):
"""Test timer configuration""" """Test timer configuration"""
with patch("ui.lib.api.check_api_status", return_value=(False, [])), patch( with (
"gradio.Timer", return_value=mock_timer patch("ui.lib.api.check_api_status", return_value=(False, [])),
patch("gradio.Timer", return_value=mock_timer),
): ):
demo = create_interface() demo = create_interface()

12
ui/lib/components/input.py
View file

@ -13,9 +13,7 @@ def create_input_column(disable_local_saving: bool = False) -> Tuple[gr.Column,
) )
# Always show file upload but handle differently based on disable_local_saving # Always show file upload but handle differently based on disable_local_saving
file_upload = gr.File( file_upload = gr.File(label="Upload Text File (.txt)", file_types=[".txt"])
label="Upload Text File (.txt)", file_types=[".txt"]
)
if not disable_local_saving: if not disable_local_saving:
# Show full interface with tabs when saving is enabled # Show full interface with tabs when saving is enabled
@ -24,7 +22,9 @@ def create_input_column(disable_local_saving: bool = False) -> Tuple[gr.Column,
tabs.selected = 0 tabs.selected = 0
# Direct Input Tab # Direct Input Tab
with gr.TabItem("Direct Input"): with gr.TabItem("Direct Input"):
text_submit_direct = gr.Button("Generate Speech", variant="primary", size="lg") text_submit_direct = gr.Button(
"Generate Speech", variant="primary", size="lg"
)
# File Input Tab # File Input Tab
with gr.TabItem("From File"): with gr.TabItem("From File"):
@ -48,7 +48,9 @@ def create_input_column(disable_local_saving: bool = False) -> Tuple[gr.Column,
) )
else: else:
# Just show the generate button when saving is disabled # Just show the generate button when saving is disabled
text_submit_direct = gr.Button("Generate Speech", variant="primary", size="lg") text_submit_direct = gr.Button(
"Generate Speech", variant="primary", size="lg"
)
tabs = None tabs = None
input_files_list = None input_files_list = None
file_preview = None file_preview = None

2
ui/lib/components/output.py
View file

@ -12,7 +12,7 @@ def create_output_column(disable_local_saving: bool = False) -> Tuple[gr.Column,
audio_output = gr.Audio( audio_output = gr.Audio(
label="Generated Speech", label="Generated Speech",
type="filepath", type="filepath",
waveform_options={"waveform_color": "#4C87AB"} waveform_options={"waveform_color": "#4C87AB"},
) )
# Create file-related components with visible=False when local saving is disabled # Create file-related components with visible=False when local saving is disabled

22
ui/lib/handlers.py
View file

@ -58,17 +58,21 @@ def setup_event_handlers(components: dict, disable_local_saving: bool = False):
def handle_file_upload(file): def handle_file_upload(file):
if file is None: if file is None:
return "" if disable_local_saving else [gr.update(choices=files.list_input_files())] return (
""
if disable_local_saving
else [gr.update(choices=files.list_input_files())]
)
try: try:
# Read the file content # Read the file content
with open(file.name, 'r', encoding='utf-8') as f: with open(file.name, "r", encoding="utf-8") as f:
text_content = f.read() text_content = f.read()
if disable_local_saving: if disable_local_saving:
# When saving is disabled, put content directly in text input # When saving is disabled, put content directly in text input
# Normalize whitespace by replacing newlines with spaces # Normalize whitespace by replacing newlines with spaces
normalized_text = ' '.join(text_content.split()) normalized_text = " ".join(text_content.split())
return normalized_text return normalized_text
else: else:
# When saving is enabled, save file and update dropdown # When saving is enabled, save file and update dropdown
@ -88,7 +92,11 @@ def setup_event_handlers(components: dict, disable_local_saving: bool = False):
except Exception as e: except Exception as e:
print(f"Error handling file: {e}") print(f"Error handling file: {e}")
return "" if disable_local_saving else [gr.update(choices=files.list_input_files())] return (
""
if disable_local_saving
else [gr.update(choices=files.list_input_files())]
)
def generate_from_text(text, voice, format, speed): def generate_from_text(text, voice, format, speed):
"""Generate speech from direct text input""" """Generate speech from direct text input"""
@ -203,7 +211,11 @@ def setup_event_handlers(components: dict, disable_local_saving: bool = False):
components["input"]["file_upload"].upload( components["input"]["file_upload"].upload(
fn=handle_file_upload, fn=handle_file_upload,
inputs=[components["input"]["file_upload"]], inputs=[components["input"]["file_upload"]],
outputs=[components["input"]["text_input"] if disable_local_saving else components["input"]["file_select"]], outputs=[
components["input"]["text_input"]
if disable_local_saving
else components["input"]["file_select"]
],
) )
if components["output"]["play_btn"] is not None: if components["output"]["play_btn"] is not None: