From e8c1284032db496a420e0427dd55c143405d42c5 Mon Sep 17 00:00:00 2001 From: remsky Date: Thu, 9 Jan 2025 18:41:44 -0700 Subject: [PATCH] Ruff format + fix --- api/src/core/config.py | 2 +- api/src/main.py | 16 +- api/src/routers/development.py | 90 +++++----- api/src/routers/openai_compatible.py | 32 ++-- api/src/services/audio.py | 74 ++++++--- api/src/services/text_processing/__init__.py | 18 +- api/src/services/text_processing/chunker.py | 17 +- .../services/text_processing/normalizer.py | 131 ++++++++++----- .../services/text_processing/phonemizer.py | 51 +++--- .../services/text_processing/vocabulary.py | 13 +- api/src/services/tts_base.py | 63 +++++-- api/src/services/tts_cpu.py | 90 +++++----- api/src/services/tts_gpu.py | 65 +++++--- api/src/services/tts_service.py | 75 ++++++--- api/src/services/warmup.py | 29 ++-- api/src/structures/schemas.py | 5 +- api/src/structures/text_schemas.py | 9 +- api/tests/conftest.py | 38 +++-- api/tests/test_audio_service.py | 6 +- api/tests/test_chunker.py | 6 +- api/tests/test_endpoints.py | 156 ++++++++++-------- api/tests/test_main.py | 14 +- api/tests/test_normalizer.py | 71 ++++++-- api/tests/test_text_processing.py | 110 ++++++------ api/tests/test_tts_implementations.py | 116 +++++++------ api/tests/test_tts_service.py | 57 ++++--- .../benchmark_first_token_stream_unified.py | 6 +- .../benchmarks/lib/stream_utils.py | 26 ++- examples/openai_streaming_audio.py | 29 ++-- .../phoneme_examples/generate_phonemes.py | 68 ++++---- examples/stream_tts_playback.py | 68 ++++---- 31 files changed, 927 insertions(+), 624 deletions(-) diff --git a/api/src/core/config.py b/api/src/core/config.py index ad0ef1c..2174bce 100644 --- a/api/src/core/config.py +++ b/api/src/core/config.py @@ -20,7 +20,7 @@ class Settings(BaseSettings): sample_rate: int = 24000 max_chunk_size: int = 300 # Maximum size of text chunks for processing gap_trim_ms: int = 250 # Amount to trim from streaming chunk ends in milliseconds - + # ONNX Optimization Settings onnx_num_threads: int = 4 # Number of threads for intra-op parallelism onnx_inter_op_threads: int = 4 # Number of threads for inter-op parallelism diff --git a/api/src/main.py b/api/src/main.py index 6051d0c..93f5f34 100644 --- a/api/src/main.py +++ b/api/src/main.py @@ -2,8 +2,8 @@ FastAPI OpenAI Compatible API """ -from contextlib import asynccontextmanager import sys +from contextlib import asynccontextmanager import uvicorn from loguru import logger @@ -12,9 +12,9 @@ from fastapi.middleware.cors import CORSMiddleware from .core.config import settings from .services.tts_model import TTSModel +from .routers.development import router as dev_router from .services.tts_service import TTSService from .routers.openai_compatible import router as openai_router -from .routers.development import router as dev_router def setup_logger(): @@ -24,25 +24,21 @@ def setup_logger(): { "sink": sys.stdout, "format": "{time:hh:mm:ss A} | " - "{level: <8} | " - "{message}", + "{level: <8} | " + "{message}", "colorize": True, - "level": "INFO" + "level": "INFO", }, ], } - # Remove default logger logger.remove() - # Add our custom logger logger.configure(**config) - # Override error colors logger.level("ERROR", color="") # Configure logger setup_logger() - @asynccontextmanager async def lifespan(app: FastAPI): """Lifespan context manager for model initialization""" @@ -52,7 +48,7 @@ async def lifespan(app: FastAPI): voicepack_count = await TTSModel.setup() # boundary = "█████╗"*9 boundary = "░" * 24 - startup_msg =f""" + startup_msg = f""" {boundary} diff --git a/api/src/routers/development.py b/api/src/routers/development.py index 6e10d4b..c7c938b 100644 --- a/api/src/routers/development.py +++ b/api/src/routers/development.py @@ -1,75 +1,77 @@ from typing import List -from loguru import logger -from fastapi import APIRouter, HTTPException, Depends, Response -from ..structures.text_schemas import PhonemeRequest, PhonemeResponse, GenerateFromPhonemesRequest -from ..services.text_processing import phonemize, tokenize -from ..services.audio import AudioService -from ..services.tts_service import TTSService -from ..services.tts_model import TTSModel + import numpy as np +from loguru import logger +from fastapi import Depends, Response, APIRouter, HTTPException + +from ..services.audio import AudioService +from ..services.tts_model import TTSModel +from ..services.tts_service import TTSService +from ..structures.text_schemas import ( + PhonemeRequest, + PhonemeResponse, + GenerateFromPhonemesRequest, +) +from ..services.text_processing import tokenize, phonemize router = APIRouter(tags=["text processing"]) + def get_tts_service() -> TTSService: """Dependency to get TTSService instance""" return TTSService() + @router.post("/text/phonemize", response_model=PhonemeResponse, tags=["deprecated"]) @router.post("/dev/phonemize", response_model=PhonemeResponse) -async def phonemize_text( - request: PhonemeRequest -) -> PhonemeResponse: +async def phonemize_text(request: PhonemeRequest) -> PhonemeResponse: """Convert text to phonemes and tokens - + Args: request: Request containing text and language tts_service: Injected TTSService instance - + Returns: Phonemes and token IDs """ try: if not request.text: raise ValueError("Text cannot be empty") - + # Get phonemes phonemes = phonemize(request.text, request.language) if not phonemes: raise ValueError("Failed to generate phonemes") - + # Get tokens tokens = tokenize(phonemes) tokens = [0] + tokens + [0] # Add start/end tokens - - return PhonemeResponse( - phonemes=phonemes, - tokens=tokens - ) + + return PhonemeResponse(phonemes=phonemes, tokens=tokens) except ValueError as e: logger.error(f"Error in phoneme generation: {str(e)}") raise HTTPException( - status_code=500, - detail={"error": "Server error", "message": str(e)} + status_code=500, detail={"error": "Server error", "message": str(e)} ) except Exception as e: logger.error(f"Error in phoneme generation: {str(e)}") raise HTTPException( - status_code=500, - detail={"error": "Server error", "message": str(e)} + status_code=500, detail={"error": "Server error", "message": str(e)} ) + @router.post("/text/generate_from_phonemes", tags=["deprecated"]) @router.post("/dev/generate_from_phonemes") async def generate_from_phonemes( request: GenerateFromPhonemesRequest, - tts_service: TTSService = Depends(get_tts_service) + tts_service: TTSService = Depends(get_tts_service), ) -> Response: """Generate audio directly from phonemes - + Args: request: Request containing phonemes and generation parameters tts_service: Injected TTSService instance - + Returns: WAV audio bytes """ @@ -77,56 +79,52 @@ async def generate_from_phonemes( if not request.phonemes: raise HTTPException( status_code=400, - detail={"error": "Invalid request", "message": "Phonemes cannot be empty"} + detail={"error": "Invalid request", "message": "Phonemes cannot be empty"}, ) - + # Validate voice exists voice_path = tts_service._get_voice_path(request.voice) if not voice_path: raise HTTPException( status_code=400, - detail={"error": "Invalid request", "message": f"Voice not found: {request.voice}"} + detail={ + "error": "Invalid request", + "message": f"Voice not found: {request.voice}", + }, ) - + try: # Load voice voicepack = tts_service._load_voice(voice_path) - + # Convert phonemes to tokens tokens = tokenize(request.phonemes) tokens = [0] + tokens + [0] # Add start/end tokens - + # Generate audio directly from tokens audio = TTSModel.generate_from_tokens(tokens, voicepack, request.speed) - + # Convert to WAV bytes wav_bytes = AudioService.convert_audio( - audio, - 24000, - "wav", - is_first_chunk=True, - is_last_chunk=True, - stream=False + audio, 24000, "wav", is_first_chunk=True, is_last_chunk=True, stream=False ) - + return Response( content=wav_bytes, media_type="audio/wav", headers={ "Content-Disposition": "attachment; filename=speech.wav", "Cache-Control": "no-cache", - } + }, ) - + except ValueError as e: logger.error(f"Invalid request: {str(e)}") raise HTTPException( - status_code=400, - detail={"error": "Invalid request", "message": str(e)} + status_code=400, detail={"error": "Invalid request", "message": str(e)} ) except Exception as e: logger.error(f"Error generating audio: {str(e)}") raise HTTPException( - status_code=500, - detail={"error": "Server error", "message": str(e)} + status_code=500, detail={"error": "Server error", "message": str(e)} ) diff --git a/api/src/routers/openai_compatible.py b/api/src/routers/openai_compatible.py index b790b4b..8c8a5d5 100644 --- a/api/src/routers/openai_compatible.py +++ b/api/src/routers/openai_compatible.py @@ -1,13 +1,12 @@ -from typing import List, Union +from typing import List, Union, AsyncGenerator from loguru import logger -from fastapi import Depends, Response, APIRouter, HTTPException -from fastapi import Header +from fastapi import Header, Depends, Response, APIRouter, HTTPException from fastapi.responses import StreamingResponse -from ..services.tts_service import TTSService + from ..services.audio import AudioService from ..structures.schemas import OpenAISpeechRequest -from typing import AsyncGenerator +from ..services.tts_service import TTSService router = APIRouter( tags=["OpenAI Compatible TTS"], @@ -20,7 +19,9 @@ def get_tts_service() -> TTSService: return TTSService() # Initialize TTSService with default settings -async def process_voices(voice_input: Union[str, List[str]], tts_service: TTSService) -> str: +async def process_voices( + voice_input: Union[str, List[str]], tts_service: TTSService +) -> str: """Process voice input into a combined voice, handling both string and list formats""" # Convert input to list of voices if isinstance(voice_input, str): @@ -35,7 +36,9 @@ async def process_voices(voice_input: Union[str, List[str]], tts_service: TTSSer available_voices = await tts_service.list_voices() for voice in voices: if voice not in available_voices: - raise ValueError(f"Voice '{voice}' not found. Available voices: {', '.join(sorted(available_voices))}") + raise ValueError( + f"Voice '{voice}' not found. Available voices: {', '.join(sorted(available_voices))}" + ) # If single voice, return it directly if len(voices) == 1: @@ -45,21 +48,23 @@ async def process_voices(voice_input: Union[str, List[str]], tts_service: TTSSer return await tts_service.combine_voices(voices=voices) -async def stream_audio_chunks(tts_service: TTSService, request: OpenAISpeechRequest) -> AsyncGenerator[bytes, None]: +async def stream_audio_chunks( + tts_service: TTSService, request: OpenAISpeechRequest +) -> AsyncGenerator[bytes, None]: """Stream audio chunks as they're generated""" voice_to_use = await process_voices(request.voice, tts_service) async for chunk in tts_service.generate_audio_stream( text=request.input, voice=voice_to_use, speed=request.speed, - output_format=request.response_format + output_format=request.response_format, ): yield chunk @router.post("/audio/speech") async def create_speech( - request: OpenAISpeechRequest, + request: OpenAISpeechRequest, tts_service: TTSService = Depends(get_tts_service), x_raw_response: str = Header(None, alias="x-raw-response"), ): @@ -101,11 +106,8 @@ async def create_speech( # Convert to requested format content = AudioService.convert_audio( - audio, - 24000, - request.response_format, - is_first_chunk=True, - stream=False) + audio, 24000, request.response_format, is_first_chunk=True, stream=False + ) return Response( content=content, diff --git a/api/src/services/audio.py b/api/src/services/audio.py index 26a6ccb..c0aeed0 100644 --- a/api/src/services/audio.py +++ b/api/src/services/audio.py @@ -6,35 +6,41 @@ import numpy as np import soundfile as sf import scipy.io.wavfile as wavfile from loguru import logger + from ..core.config import settings + class AudioNormalizer: """Handles audio normalization state for a single stream""" + def __init__(self): self.int16_max = np.iinfo(np.int16).max self.chunk_trim_ms = settings.gap_trim_ms self.sample_rate = 24000 # Sample rate of the audio self.samples_to_trim = int(self.chunk_trim_ms * self.sample_rate / 1000) - - def normalize(self, audio_data: np.ndarray, is_last_chunk: bool = False) -> np.ndarray: + + def normalize( + self, audio_data: np.ndarray, is_last_chunk: bool = False + ) -> np.ndarray: """Normalize audio data to int16 range and trim chunk boundaries""" # Convert to float32 if not already audio_float = audio_data.astype(np.float32) - + # Normalize to [-1, 1] range first if np.max(np.abs(audio_float)) > 0: audio_float = audio_float / np.max(np.abs(audio_float)) - + # Trim end of non-final chunks to reduce gaps if not is_last_chunk and len(audio_float) > self.samples_to_trim: - audio_float = audio_float[:-self.samples_to_trim] - + audio_float = audio_float[: -self.samples_to_trim] + # Scale to int16 range return (audio_float * self.int16_max).astype(np.int16) + class AudioService: """Service for audio format conversions""" - + # Default audio format settings balanced for speed and compression DEFAULT_SETTINGS = { "mp3": { @@ -46,19 +52,19 @@ class AudioService: }, "flac": { "compression_level": 0.0, # Light compression, still fast - } + }, } - + @staticmethod def convert_audio( - audio_data: np.ndarray, - sample_rate: int, - output_format: str, + audio_data: np.ndarray, + sample_rate: int, + output_format: str, is_first_chunk: bool = True, is_last_chunk: bool = False, normalizer: AudioNormalizer = None, format_settings: dict = None, - stream: bool = True + stream: bool = True, ) -> bytes: """Convert audio data to specified format @@ -90,37 +96,55 @@ class AudioService: # Always normalize audio to ensure proper amplitude scaling if normalizer is None: normalizer = AudioNormalizer() - normalized_audio = normalizer.normalize(audio_data, is_last_chunk=is_last_chunk) + normalized_audio = normalizer.normalize( + audio_data, is_last_chunk=is_last_chunk + ) if output_format == "pcm": # Raw 16-bit PCM samples, no header buffer.write(normalized_audio.tobytes()) elif output_format == "wav": # Always use soundfile for WAV to ensure proper headers and normalization - sf.write(buffer, normalized_audio, sample_rate, format="WAV", subtype='PCM_16') + sf.write( + buffer, + normalized_audio, + sample_rate, + format="WAV", + subtype="PCM_16", + ) elif output_format == "mp3": # Use format settings or defaults settings = format_settings.get("mp3", {}) if format_settings else {} settings = {**AudioService.DEFAULT_SETTINGS["mp3"], **settings} sf.write( - buffer, normalized_audio, - sample_rate, format="MP3", - **settings - ) - + buffer, normalized_audio, sample_rate, format="MP3", **settings + ) + elif output_format == "opus": settings = format_settings.get("opus", {}) if format_settings else {} settings = {**AudioService.DEFAULT_SETTINGS["opus"], **settings} - sf.write(buffer, normalized_audio, sample_rate, format="OGG", - subtype="OPUS", **settings) - + sf.write( + buffer, + normalized_audio, + sample_rate, + format="OGG", + subtype="OPUS", + **settings, + ) + elif output_format == "flac": if is_first_chunk: logger.info("Starting FLAC stream...") settings = format_settings.get("flac", {}) if format_settings else {} settings = {**AudioService.DEFAULT_SETTINGS["flac"], **settings} - sf.write(buffer, normalized_audio, sample_rate, format="FLAC", - subtype='PCM_16', **settings) + sf.write( + buffer, + normalized_audio, + sample_rate, + format="FLAC", + subtype="PCM_16", + **settings, + ) else: if output_format == "aac": raise ValueError( diff --git a/api/src/services/text_processing/__init__.py b/api/src/services/text_processing/__init__.py index f945e18..624ce7c 100644 --- a/api/src/services/text_processing/__init__.py +++ b/api/src/services/text_processing/__init__.py @@ -1,13 +1,13 @@ from .normalizer import normalize_text -from .phonemizer import phonemize, PhonemizerBackend, EspeakBackend -from .vocabulary import tokenize, decode_tokens, VOCAB +from .phonemizer import EspeakBackend, PhonemizerBackend, phonemize +from .vocabulary import VOCAB, tokenize, decode_tokens __all__ = [ - 'normalize_text', - 'phonemize', - 'tokenize', - 'decode_tokens', - 'VOCAB', - 'PhonemizerBackend', - 'EspeakBackend' + "normalize_text", + "phonemize", + "tokenize", + "decode_tokens", + "VOCAB", + "PhonemizerBackend", + "EspeakBackend", ] diff --git a/api/src/services/text_processing/chunker.py b/api/src/services/text_processing/chunker.py index c0c59eb..2bbda79 100644 --- a/api/src/services/text_processing/chunker.py +++ b/api/src/services/text_processing/chunker.py @@ -1,44 +1,45 @@ """Text chunking service""" import re + from ...core.config import settings def split_text(text: str, max_chunk=None): """Split text into chunks on natural pause points - + Args: text: Text to split into chunks max_chunk: Maximum chunk size (defaults to settings.max_chunk_size) """ if max_chunk is None: max_chunk = settings.max_chunk_size - + if not isinstance(text, str): text = str(text) if text is not None else "" - + text = text.strip() if not text: return - + # First split into sentences sentences = re.split(r"(?<=[.!?])\s+", text) - + for sentence in sentences: sentence = sentence.strip() if not sentence: continue - + # For medium-length sentences, split on punctuation if len(sentence) > max_chunk: # Lower threshold for more consistent sizes # First try splitting on semicolons and colons parts = re.split(r"(?<=[;:])\s+", sentence) - + for part in parts: part = part.strip() if not part: continue - + # If part is still long, split on commas if len(part) > max_chunk: subparts = re.split(r"(?<=,)\s+", part) diff --git a/api/src/services/text_processing/normalizer.py b/api/src/services/text_processing/normalizer.py index 3cc4cc2..383abbd 100644 --- a/api/src/services/text_processing/normalizer.py +++ b/api/src/services/text_processing/normalizer.py @@ -9,19 +9,58 @@ from functools import lru_cache # Constants VALID_TLDS = [ - "com", "org", "net", "edu", "gov", "mil", "int", "biz", "info", "name", - "pro", "coop", "museum", "travel", "jobs", "mobi", "tel", "asia", "cat", - "xxx", "aero", "arpa", "bg", "br", "ca", "cn", "de", "es", "eu", "fr", - "in", "it", "jp", "mx", "nl", "ru", "uk", "us", "io" + "com", + "org", + "net", + "edu", + "gov", + "mil", + "int", + "biz", + "info", + "name", + "pro", + "coop", + "museum", + "travel", + "jobs", + "mobi", + "tel", + "asia", + "cat", + "xxx", + "aero", + "arpa", + "bg", + "br", + "ca", + "cn", + "de", + "es", + "eu", + "fr", + "in", + "it", + "jp", + "mx", + "nl", + "ru", + "uk", + "us", + "io", ] # Pre-compiled regex patterns for performance -EMAIL_PATTERN = re.compile(r"\b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-z]{2,}\b", re.IGNORECASE) -URL_PATTERN = re.compile( - r"(https?://|www\.|)+(localhost|[a-zA-Z0-9.-]+(\.(?:" + - "|".join(VALID_TLDS) + "))+|[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})(:[0-9]+)?([/?][^\s]*)?", - re.IGNORECASE +EMAIL_PATTERN = re.compile( + r"\b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-z]{2,}\b", re.IGNORECASE ) +URL_PATTERN = re.compile( + r"(https?://|www\.|)+(localhost|[a-zA-Z0-9.-]+(\.(?:" + + "|".join(VALID_TLDS) + + "))+|[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})(:[0-9]+)?([/?][^\s]*)?", + re.IGNORECASE, +) + def split_num(num: re.Match[str]) -> str: """Handle number splitting for various formats""" @@ -47,6 +86,7 @@ def split_num(num: re.Match[str]) -> str: return f"{left} oh {right}{s}" return f"{left} {right}{s}" + def handle_money(m: re.Match[str]) -> str: """Convert money expressions to spoken form""" m = m.group() @@ -66,49 +106,57 @@ def handle_money(m: re.Match[str]) -> str: ) return f"{b} {bill}{s} and {c} {coins}" + def handle_decimal(num: re.Match[str]) -> str: """Convert decimal numbers to spoken form""" a, b = num.group().split(".") return " point ".join([a, " ".join(b)]) + def handle_email(m: re.Match[str]) -> str: """Convert email addresses into speakable format""" email = m.group(0) - parts = email.split('@') + parts = email.split("@") if len(parts) == 2: user, domain = parts - domain = domain.replace('.', ' dot ') + domain = domain.replace(".", " dot ") return f"{user} at {domain}" return email + def handle_url(u: re.Match[str]) -> str: """Make URLs speakable by converting special characters to spoken words""" if not u: return "" - + url = u.group(0).strip() - + # Handle protocol first - url = re.sub(r'^https?://', lambda a: 'https ' if 'https' in a.group() else 'http ', url, flags=re.IGNORECASE) - url = re.sub(r'^www\.', 'www ', url, flags=re.IGNORECASE) - + url = re.sub( + r"^https?://", + lambda a: "https " if "https" in a.group() else "http ", + url, + flags=re.IGNORECASE, + ) + url = re.sub(r"^www\.", "www ", url, flags=re.IGNORECASE) + # Handle port numbers before other replacements - url = re.sub(r':(\d+)(?=/|$)', lambda m: f" colon {m.group(1)}", url) - + url = re.sub(r":(\d+)(?=/|$)", lambda m: f" colon {m.group(1)}", url) + # Split into domain and path - parts = url.split('/', 1) + parts = url.split("/", 1) domain = parts[0] - path = parts[1] if len(parts) > 1 else '' - + path = parts[1] if len(parts) > 1 else "" + # Handle dots in domain - domain = domain.replace('.', ' dot ') - + domain = domain.replace(".", " dot ") + # Reconstruct URL if path: url = f"{domain} slash {path}" else: url = domain - + # Replace remaining symbols with words url = url.replace("-", " dash ") url = url.replace("_", " underscore ") @@ -118,56 +166,55 @@ def handle_url(u: re.Match[str]) -> str: url = url.replace("%", " percent ") url = url.replace(":", " colon ") # Handle any remaining colons url = url.replace("/", " slash ") # Handle any remaining slashes - + # Clean up extra spaces - return re.sub(r'\s+', ' ', url).strip() + return re.sub(r"\s+", " ", url).strip() def normalize_urls(text: str) -> str: """Pre-process URLs before other text normalization""" # Handle email addresses first text = EMAIL_PATTERN.sub(handle_email, text) - + # Handle URLs text = URL_PATTERN.sub(handle_url, text) - + return text - + + def normalize_text(text: str) -> str: """Normalize text for TTS processing""" # Pre-process URLs first text = normalize_urls(text) - + # Replace quotes and brackets text = text.replace(chr(8216), "'").replace(chr(8217), "'") text = text.replace("«", chr(8220)).replace("»", chr(8221)) text = text.replace(chr(8220), '"').replace(chr(8221), '"') text = text.replace("(", "«").replace(")", "»") - + # Handle CJK punctuation for a, b in zip("、。！，：；？", ",.!,:;?"): text = text.replace(a, b + " ") - + # Clean up whitespace text = re.sub(r"[^\S \n]", " ", text) text = re.sub(r" +", " ", text) text = re.sub(r"(?<=\n) +(?=\n)", "", text) - + # Handle titles and abbreviations text = re.sub(r"\bD[Rr]\.(?= [A-Z])", "Doctor", text) text = re.sub(r"\b(?:Mr\.|MR\.(?= [A-Z]))", "Mister", text) text = re.sub(r"\b(?:Ms\.|MS\.(?= [A-Z]))", "Miss", text) text = re.sub(r"\b(?:Mrs\.|MRS\.(?= [A-Z]))", "Mrs", text) text = re.sub(r"\betc\.(?! [A-Z])", "etc", text) - + # Handle common words text = re.sub(r"(?i)\b(y)eah?\b", r"\1e'a", text) - + # Handle numbers and money text = re.sub( - r"\d*\.\d+|\b\d{4}s?\b|(? str: text, ) text = re.sub(r"\d*\.\d+", handle_decimal, text) - + # Handle various formatting text = re.sub(r"(?<=\d)-(?=\d)", " to ", text) text = re.sub(r"(?<=\d)S", " S", text) text = re.sub(r"(?<=[BCDFGHJ-NP-TV-Z])'?s\b", "'S", text) text = re.sub(r"(?<=X')S\b", "s", text) 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) - + return text.strip() diff --git a/api/src/services/text_processing/phonemizer.py b/api/src/services/text_processing/phonemizer.py index 0d04d86..a328bb5 100644 --- a/api/src/services/text_processing/phonemizer.py +++ b/api/src/services/text_processing/phonemizer.py @@ -1,97 +1,98 @@ import re from abc import ABC, abstractmethod + import phonemizer + from .normalizer import normalize_text + class PhonemizerBackend(ABC): """Abstract base class for phonemization backends""" - + @abstractmethod def phonemize(self, text: str) -> str: """Convert text to phonemes - + Args: text: Text to convert to phonemes - + Returns: Phonemized text """ pass + class EspeakBackend(PhonemizerBackend): """Espeak-based phonemizer implementation""" - + def __init__(self, language: str): """Initialize espeak backend - + Args: language: Language code ('en-us' or 'en-gb') """ self.backend = phonemizer.backend.EspeakBackend( - language=language, - preserve_punctuation=True, - with_stress=True + language=language, preserve_punctuation=True, with_stress=True ) self.language = language - + def phonemize(self, text: str) -> str: """Convert text to phonemes using espeak - + Args: text: Text to convert to phonemes - + Returns: Phonemized text """ # Phonemize text ps = self.backend.phonemize([text]) ps = ps[0] if ps else "" - + # Handle special cases ps = ps.replace("kəkˈoːɹoʊ", "kˈoʊkəɹoʊ").replace("kəkˈɔːɹəʊ", "kˈəʊkəɹəʊ") ps = ps.replace("ʲ", "j").replace("r", "ɹ").replace("x", "k").replace("ɬ", "l") ps = re.sub(r"(?<=[a-zɹː])(?=hˈʌndɹɪd)", " ", ps) ps = re.sub(r' z(?=[;:,.!?¡¿—…"«»"" ]|$)', "z", ps) - + # Language-specific rules if self.language == "en-us": ps = re.sub(r"(?<=nˈaɪn)ti(?!ː)", "di", ps) - + return ps.strip() + def create_phonemizer(language: str = "a") -> PhonemizerBackend: """Factory function to create phonemizer backend - + Args: language: Language code ('a' for US English, 'b' for British English) - + Returns: Phonemizer backend instance """ # Map language codes to espeak language codes - lang_map = { - "a": "en-us", - "b": "en-gb" - } - + lang_map = {"a": "en-us", "b": "en-gb"} + if language not in lang_map: raise ValueError(f"Unsupported language code: {language}") - + return EspeakBackend(lang_map[language]) + def phonemize(text: str, language: str = "a", normalize: bool = True) -> str: """Convert text to phonemes - + Args: text: Text to convert to phonemes language: Language code ('a' for US English, 'b' for British English) normalize: Whether to normalize text before phonemization - + Returns: Phonemized text """ if normalize: text = normalize_text(text) - + phonemizer = create_phonemizer(language) return phonemizer.phonemize(text) diff --git a/api/src/services/text_processing/vocabulary.py b/api/src/services/text_processing/vocabulary.py index 66af961..7a12892 100644 --- a/api/src/services/text_processing/vocabulary.py +++ b/api/src/services/text_processing/vocabulary.py @@ -4,31 +4,34 @@ def get_vocab(): _punctuation = ';:,.!?¡¿—…"«»"" ' _letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" - + # Create vocabulary dictionary symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa) return {symbol: i for i, symbol in enumerate(symbols)} + # Initialize vocabulary VOCAB = get_vocab() + def tokenize(phonemes: str) -> list[int]: """Convert phonemes string to token IDs - + Args: phonemes: String of phonemes to tokenize - + Returns: List of token IDs """ return [i for i in map(VOCAB.get, phonemes) if i is not None] + def decode_tokens(tokens: list[int]) -> str: """Convert token IDs back to phonemes string - + Args: tokens: List of token IDs - + Returns: String of phonemes """ diff --git a/api/src/services/tts_base.py b/api/src/services/tts_base.py index 399aa2d..6076ebf 100644 --- a/api/src/services/tts_base.py +++ b/api/src/services/tts_base.py @@ -2,12 +2,14 @@ import os import threading from abc import ABC, abstractmethod from typing import List, Tuple -import torch + import numpy as np +import torch from loguru import logger from ..core.config import settings + class TTSBaseModel(ABC): _instance = None _lock = threading.Lock() @@ -26,7 +28,9 @@ class TTSBaseModel(ABC): # Test CUDA device test_tensor = torch.zeros(1).cuda() logger.info("CUDA test successful") - model_path = os.path.join(settings.model_dir, settings.pytorch_model_path) + model_path = os.path.join( + settings.model_dir, settings.pytorch_model_path + ) cls._device = "cuda" except Exception as e: logger.error(f"CUDA test failed: {e}") @@ -54,19 +58,35 @@ class TTSBaseModel(ABC): voice_path = os.path.join(cls.VOICES_DIR, file) if not os.path.exists(voice_path): try: - logger.info(f"Copying base voice {voice_name} to voices directory") + logger.info( + f"Copying base voice {voice_name} to voices directory" + ) base_path = os.path.join(base_voices_dir, file) - voicepack = torch.load(base_path, map_location=cls._device, weights_only=True) + voicepack = torch.load( + base_path, + map_location=cls._device, + weights_only=True, + ) torch.save(voicepack, voice_path) except Exception as e: - logger.error(f"Error copying voice {voice_name}: {str(e)}") + logger.error( + f"Error copying voice {voice_name}: {str(e)}" + ) # Count voices in directory - voice_count = len([f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")]) + voice_count = len( + [f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")] + ) # Now that model and voices are ready, do warmup try: - with open(os.path.join(os.path.dirname(os.path.dirname(__file__)), "core", "don_quixote.txt")) as f: + with open( + os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "core", + "don_quixote.txt", + ) + ) as f: warmup_text = f.read() except Exception as e: logger.warning(f"Failed to load warmup text: {e}") @@ -74,16 +94,19 @@ class TTSBaseModel(ABC): # Use warmup service after model is fully initialized from .warmup import WarmupService + warmup = WarmupService() - + # Load and warm up voices loaded_voices = warmup.load_voices() await warmup.warmup_voices(warmup_text, loaded_voices) - + logger.info("Model warm-up complete") # Count voices in directory - voice_count = len([f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")]) + voice_count = len( + [f for f in os.listdir(cls.VOICES_DIR) if f.endswith(".pt")] + ) return voice_count @classmethod @@ -96,11 +119,11 @@ class TTSBaseModel(ABC): @abstractmethod def process_text(cls, text: str, language: str) -> Tuple[str, List[int]]: """Process text into phonemes and tokens - + Args: text: Input text language: Language code - + Returns: tuple[str, list[int]]: Phonemes and token IDs """ @@ -108,15 +131,17 @@ class TTSBaseModel(ABC): @classmethod @abstractmethod - def generate_from_text(cls, text: str, voicepack: torch.Tensor, language: str, speed: float) -> Tuple[np.ndarray, str]: + def generate_from_text( + cls, text: str, voicepack: torch.Tensor, language: str, speed: float + ) -> Tuple[np.ndarray, str]: """Generate audio from text - + Args: text: Input text voicepack: Voice tensor language: Language code speed: Speed factor - + Returns: tuple[np.ndarray, str]: Generated audio samples and phonemes """ @@ -124,14 +149,16 @@ class TTSBaseModel(ABC): @classmethod @abstractmethod - def generate_from_tokens(cls, tokens: List[int], voicepack: torch.Tensor, speed: float) -> np.ndarray: + def generate_from_tokens( + cls, tokens: List[int], voicepack: torch.Tensor, speed: float + ) -> np.ndarray: """Generate audio from tokens - + Args: tokens: Token IDs voicepack: Voice tensor speed: Speed factor - + Returns: np.ndarray: Generated audio samples """ diff --git a/api/src/services/tts_cpu.py b/api/src/services/tts_cpu.py index 79c0c75..3ee3395 100644 --- a/api/src/services/tts_cpu.py +++ b/api/src/services/tts_cpu.py @@ -1,12 +1,19 @@ import os + import numpy as np import torch -from onnxruntime import InferenceSession, SessionOptions, GraphOptimizationLevel, ExecutionMode from loguru import logger +from onnxruntime import ( + ExecutionMode, + SessionOptions, + InferenceSession, + GraphOptimizationLevel, +) from .tts_base import TTSBaseModel -from .text_processing import phonemize, tokenize from ..core.config import settings +from .text_processing import tokenize, phonemize + class TTSCPUModel(TTSBaseModel): _instance = None @@ -35,45 +42,51 @@ class TTSCPUModel(TTSBaseModel): return None logger.info(f"Loading ONNX model from {onnx_path}") - + # Configure ONNX session for optimal performance session_options = SessionOptions() - + # Set optimization level if settings.onnx_optimization_level == "all": - session_options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL + session_options.graph_optimization_level = ( + GraphOptimizationLevel.ORT_ENABLE_ALL + ) elif settings.onnx_optimization_level == "basic": - session_options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC + session_options.graph_optimization_level = ( + GraphOptimizationLevel.ORT_ENABLE_BASIC + ) else: - session_options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL - + session_options.graph_optimization_level = ( + GraphOptimizationLevel.ORT_DISABLE_ALL + ) + # Configure threading session_options.intra_op_num_threads = settings.onnx_num_threads session_options.inter_op_num_threads = settings.onnx_inter_op_threads - + # Set execution mode session_options.execution_mode = ( - ExecutionMode.ORT_PARALLEL - if settings.onnx_execution_mode == "parallel" + ExecutionMode.ORT_PARALLEL + if settings.onnx_execution_mode == "parallel" else ExecutionMode.ORT_SEQUENTIAL ) - + # Enable/disable memory pattern optimization session_options.enable_mem_pattern = settings.onnx_memory_pattern # Configure CPU provider options provider_options = { - 'CPUExecutionProvider': { - 'arena_extend_strategy': settings.onnx_arena_extend_strategy, - 'cpu_memory_arena_cfg': 'cpu:0' + "CPUExecutionProvider": { + "arena_extend_strategy": settings.onnx_arena_extend_strategy, + "cpu_memory_arena_cfg": "cpu:0", } } session = InferenceSession( onnx_path, sess_options=session_options, - providers=['CPUExecutionProvider'], - provider_options=[provider_options] + providers=["CPUExecutionProvider"], + provider_options=[provider_options], ) cls._onnx_session = session return session @@ -82,11 +95,11 @@ class TTSCPUModel(TTSBaseModel): @classmethod def process_text(cls, text: str, language: str) -> tuple[str, list[int]]: """Process text into phonemes and tokens - + Args: text: Input text language: Language code - + Returns: tuple[str, list[int]]: Phonemes and token IDs """ @@ -96,38 +109,42 @@ class TTSCPUModel(TTSBaseModel): return phonemes, tokens @classmethod - def generate_from_text(cls, text: str, voicepack: torch.Tensor, language: str, speed: float) -> tuple[np.ndarray, str]: + def generate_from_text( + cls, text: str, voicepack: torch.Tensor, language: str, speed: float + ) -> tuple[np.ndarray, str]: """Generate audio from text - + Args: text: Input text voicepack: Voice tensor language: Language code speed: Speed factor - + Returns: tuple[np.ndarray, str]: Generated audio samples and phonemes """ if cls._onnx_session is None: raise RuntimeError("ONNX model not initialized") - + # Process text phonemes, tokens = cls.process_text(text, language) - + # Generate audio audio = cls.generate_from_tokens(tokens, voicepack, speed) - + return audio, phonemes @classmethod - def generate_from_tokens(cls, tokens: list[int], voicepack: torch.Tensor, speed: float) -> np.ndarray: + def generate_from_tokens( + cls, tokens: list[int], voicepack: torch.Tensor, speed: float + ) -> np.ndarray: """Generate audio from tokens - + Args: tokens: Token IDs voicepack: Voice tensor speed: Speed factor - + Returns: np.ndarray: Generated audio samples """ @@ -136,16 +153,15 @@ class TTSCPUModel(TTSBaseModel): # Pre-allocate and prepare inputs tokens_input = np.array([tokens], dtype=np.int64) - style_input = voicepack[len(tokens)-2].numpy() # Already has correct dimensions - speed_input = np.full(1, speed, dtype=np.float32) # More efficient than ones * speed - + style_input = voicepack[ + len(tokens) - 2 + ].numpy() # Already has correct dimensions + speed_input = np.full( + 1, speed, dtype=np.float32 + ) # More efficient than ones * speed + # Run inference with optimized inputs result = cls._onnx_session.run( - None, - { - 'tokens': tokens_input, - 'style': style_input, - 'speed': speed_input - } + None, {"tokens": tokens_input, "style": style_input, "speed": speed_input} ) return result[0] diff --git a/api/src/services/tts_gpu.py b/api/src/services/tts_gpu.py index 0fc3397..87e9ef2 100644 --- a/api/src/services/tts_gpu.py +++ b/api/src/services/tts_gpu.py @@ -1,13 +1,15 @@ import os +import time + import numpy as np import torch -import time from loguru import logger from models import build_model -from .text_processing import phonemize, tokenize from .tts_base import TTSBaseModel from ..core.config import settings +from .text_processing import tokenize, phonemize + # @torch.no_grad() # def forward(model, tokens, ref_s, speed): @@ -38,47 +40,48 @@ from ..core.config import settings def forward(model, tokens, ref_s, speed): """Forward pass through the model with light optimizations that preserve output quality""" device = ref_s.device - + # Keep original token handling but optimize device placement tokens = torch.LongTensor([[0, *tokens, 0]]).to(device) input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) text_mask = length_to_mask(input_lengths).to(device) - + # BERT and encoder pass bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) d_en = model.bert_encoder(bert_dur).transpose(-1, -2) - + # Split reference signal once for efficiency s_content = ref_s[:, 128:] s_ref = ref_s[:, :128] - + # Predictor forward pass d = model.predictor.text_encoder(d_en, s_content, input_lengths, text_mask) x, _ = model.predictor.lstm(d) - + # Duration prediction - keeping original logic duration = model.predictor.duration_proj(x) duration = torch.sigmoid(duration).sum(axis=-1) / speed pred_dur = torch.round(duration).clamp(min=1).long() - + # Alignment matrix construction - keeping original approach for quality pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item(), device=device) c_frame = 0 for i in range(pred_aln_trg.size(0)): - pred_aln_trg[i, c_frame:c_frame + pred_dur[0, i].item()] = 1 + pred_aln_trg[i, c_frame : c_frame + pred_dur[0, i].item()] = 1 c_frame += pred_dur[0, i].item() - + # Matrix multiplications - reuse unsqueezed tensor pred_aln_trg = pred_aln_trg.unsqueeze(0) # Do unsqueeze once en = d.transpose(-1, -2) @ pred_aln_trg F0_pred, N_pred = model.predictor.F0Ntrain(en, s_content) - + # Text encoding and final decoding t_en = model.text_encoder(tokens, input_lengths, text_mask) asr = t_en @ pred_aln_trg - + return model.decoder(asr, F0_pred, N_pred, s_ref).squeeze().cpu().numpy() + # def length_to_mask(lengths): # """Create attention mask from lengths""" # mask = ( @@ -90,17 +93,21 @@ def forward(model, tokens, ref_s, speed): # mask = torch.gt(mask + 1, lengths.unsqueeze(1)) # return mask + def length_to_mask(lengths): """Create attention mask from lengths - possibly optimized version""" max_len = lengths.max() # Create mask directly on the same device as lengths - mask = torch.arange(max_len, device=lengths.device)[None, :].expand(lengths.shape[0], -1) + mask = torch.arange(max_len, device=lengths.device)[None, :].expand( + lengths.shape[0], -1 + ) # Avoid type_as by using the correct dtype from the start if lengths.dtype != mask.dtype: mask = mask.to(dtype=lengths.dtype) # Fuse operations using broadcasting return mask + 1 > lengths[:, None] + class TTSGPUModel(TTSBaseModel): _instance = None _device = "cuda" @@ -130,11 +137,11 @@ class TTSGPUModel(TTSBaseModel): @classmethod def process_text(cls, text: str, language: str) -> tuple[str, list[int]]: """Process text into phonemes and tokens - + Args: text: Input text language: Language code - + Returns: tuple[str, list[int]]: Phonemes and token IDs """ @@ -143,48 +150,52 @@ class TTSGPUModel(TTSBaseModel): return phonemes, tokens @classmethod - def generate_from_text(cls, text: str, voicepack: torch.Tensor, language: str, speed: float) -> tuple[np.ndarray, str]: + def generate_from_text( + cls, text: str, voicepack: torch.Tensor, language: str, speed: float + ) -> tuple[np.ndarray, str]: """Generate audio from text - + Args: text: Input text voicepack: Voice tensor language: Language code speed: Speed factor - + Returns: tuple[np.ndarray, str]: Generated audio samples and phonemes """ if cls._instance is None: raise RuntimeError("GPU model not initialized") - + # Process text phonemes, tokens = cls.process_text(text, language) - + # Generate audio audio = cls.generate_from_tokens(tokens, voicepack, speed) - + return audio, phonemes @classmethod - def generate_from_tokens(cls, tokens: list[int], voicepack: torch.Tensor, speed: float) -> np.ndarray: + def generate_from_tokens( + cls, tokens: list[int], voicepack: torch.Tensor, speed: float + ) -> np.ndarray: """Generate audio from tokens - + Args: tokens: Token IDs voicepack: Voice tensor speed: Speed factor - + Returns: np.ndarray: Generated audio samples """ if cls._instance is None: raise RuntimeError("GPU model not initialized") - + # Get reference style ref_s = voicepack[len(tokens)] - + # Generate audio audio = forward(cls._instance, tokens, ref_s, speed) - + return audio diff --git a/api/src/services/tts_service.py b/api/src/services/tts_service.py index 10f743c..bbd35b8 100644 --- a/api/src/services/tts_service.py +++ b/api/src/services/tts_service.py @@ -1,5 +1,4 @@ import io -import aiofiles.os import os import re import time @@ -8,13 +7,14 @@ from functools import lru_cache import numpy as np import torch +import aiofiles.os import scipy.io.wavfile as wavfile -from .text_processing import normalize_text, chunker from loguru import logger -from ..core.config import settings -from .tts_model import TTSModel from .audio import AudioService, AudioNormalizer +from .tts_model import TTSModel +from ..core.config import settings +from .text_processing import chunker, normalize_text class TTSService: @@ -26,7 +26,9 @@ class TTSService: @lru_cache(maxsize=3) # Cache up to 3 most recently used voices def _load_voice(voice_path: str) -> torch.Tensor: """Load and cache a voice model""" - return torch.load(voice_path, map_location=TTSModel.get_device(), weights_only=True) + return torch.load( + voice_path, map_location=TTSModel.get_device(), weights_only=True + ) def _get_voice_path(self, voice_name: str) -> Optional[str]: """Get the path to a voice file""" @@ -37,7 +39,9 @@ class TTSService: self, text: str, voice: str, speed: float, stitch_long_output: bool = True ) -> Tuple[torch.Tensor, float]: """Generate complete audio and return with processing time""" - audio, processing_time = self._generate_audio_internal(text, voice, speed, stitch_long_output) + audio, processing_time = self._generate_audio_internal( + text, voice, speed, stitch_long_output + ) return audio, processing_time def _generate_audio_internal( @@ -72,7 +76,9 @@ class TTSService: phonemes, tokens = TTSModel.process_text(chunk, voice[0]) chunks_data.append((chunk, tokens)) except Exception as e: - logger.error(f"Failed to process chunk: '{chunk}'. Error: {str(e)}") + logger.error( + f"Failed to process chunk: '{chunk}'. Error: {str(e)}" + ) continue if not chunks_data: @@ -82,20 +88,28 @@ class TTSService: audio_chunks = [] for chunk, tokens in chunks_data: try: - chunk_audio = TTSModel.generate_from_tokens(tokens, voicepack, speed) + chunk_audio = TTSModel.generate_from_tokens( + tokens, voicepack, speed + ) if chunk_audio is not None: audio_chunks.append(chunk_audio) else: logger.error(f"No audio generated for chunk: '{chunk}'") except Exception as e: - logger.error(f"Failed to generate audio for chunk: '{chunk}'. Error: {str(e)}") + logger.error( + f"Failed to generate audio for chunk: '{chunk}'. Error: {str(e)}" + ) continue if not audio_chunks: raise ValueError("No audio chunks were generated successfully") # Concatenate all chunks - audio = np.concatenate(audio_chunks) if len(audio_chunks) > 1 else audio_chunks[0] + audio = ( + np.concatenate(audio_chunks) + if len(audio_chunks) > 1 + else audio_chunks[0] + ) else: # Process single chunk phonemes, tokens = TTSModel.process_text(text, voice[0]) @@ -109,14 +123,19 @@ class TTSService: raise async def generate_audio_stream( - self, text: str, voice: str, speed: float, output_format: str = "wav", silent=False + self, + text: str, + voice: str, + speed: float, + output_format: str = "wav", + silent=False, ): """Generate and yield audio chunks as they're generated for real-time streaming""" try: stream_start = time.time() # Create normalizer for consistent audio levels stream_normalizer = AudioNormalizer() - + # Input validation and preprocessing if not text: raise ValueError("Text is empty") @@ -125,7 +144,9 @@ class TTSService: if not normalized: raise ValueError("Text is empty after preprocessing") text = str(normalized) - logger.debug(f"Text preprocessing took: {(time.time() - preprocess_start)*1000:.1f}ms") + logger.debug( + f"Text preprocessing took: {(time.time() - preprocess_start)*1000:.1f}ms" + ) # Voice validation and loading voice_start = time.time() @@ -133,24 +154,28 @@ class TTSService: if not voice_path: raise ValueError(f"Voice not found: {voice}") voicepack = self._load_voice(voice_path) - logger.debug(f"Voice loading took: {(time.time() - voice_start)*1000:.1f}ms") + logger.debug( + f"Voice loading took: {(time.time() - voice_start)*1000:.1f}ms" + ) # Process chunks as they're generated is_first = True chunks_processed = 0 - + # Process chunks as they come from generator chunk_gen = chunker.split_text(text) current_chunk = next(chunk_gen, None) - + while current_chunk is not None: next_chunk = next(chunk_gen, None) # Peek at next chunk chunks_processed += 1 try: # Process text and generate audio phonemes, tokens = TTSModel.process_text(current_chunk, voice[0]) - chunk_audio = TTSModel.generate_from_tokens(tokens, voicepack, speed) - + chunk_audio = TTSModel.generate_from_tokens( + tokens, voicepack, speed + ) + if chunk_audio is not None: # Convert chunk with proper header handling chunk_bytes = AudioService.convert_audio( @@ -159,19 +184,21 @@ class TTSService: output_format, is_first_chunk=is_first, normalizer=stream_normalizer, - is_last_chunk=(next_chunk is None) # Last if no next chunk + is_last_chunk=(next_chunk is None), # Last if no next chunk ) - + yield chunk_bytes is_first = False else: logger.error(f"No audio generated for chunk: '{current_chunk}'") except Exception as e: - logger.error(f"Failed to generate audio for chunk: '{current_chunk}'. Error: {str(e)}") - + logger.error( + f"Failed to generate audio for chunk: '{current_chunk}'. Error: {str(e)}" + ) + current_chunk = next_chunk # Move to next chunk - + except Exception as e: logger.error(f"Error in audio generation stream: {str(e)}") raise @@ -227,7 +254,7 @@ class TTSService: if not isinstance(e, (ValueError, RuntimeError)): raise RuntimeError(f"Error combining voices: {str(e)}") raise - + async def list_voices(self) -> List[str]: """List all available voices""" voices = [] diff --git a/api/src/services/warmup.py b/api/src/services/warmup.py index 72ede02..1be2013 100644 --- a/api/src/services/warmup.py +++ b/api/src/services/warmup.py @@ -1,57 +1,58 @@ import os from typing import List, Tuple + import torch from loguru import logger -from .tts_service import TTSService from .tts_model import TTSModel +from .tts_service import TTSService from ..core.config import settings class WarmupService: """Service for warming up TTS models and voice caches""" - + def __init__(self): """Initialize warmup service and ensure model is ready""" # Initialize model if not already initialized if TTSModel._instance is None: TTSModel.initialize(settings.model_dir) self.tts_service = TTSService() - + def load_voices(self) -> List[Tuple[str, torch.Tensor]]: """Load and cache voices up to LRU limit""" # Get all voices sorted by filename length (shorter names first, usually base voices) voice_files = sorted( - [f for f in os.listdir(TTSModel.VOICES_DIR) if f.endswith(".pt")], - key=len + [f for f in os.listdir(TTSModel.VOICES_DIR) if f.endswith(".pt")], key=len ) - - n_voices_cache=1 + + n_voices_cache = 1 loaded_voices = [] for voice_file in voice_files[:n_voices_cache]: try: voice_path = os.path.join(TTSModel.VOICES_DIR, voice_file) # load using service, lru cache voicepack = self.tts_service._load_voice(voice_path) - loaded_voices.append((voice_file[:-3], voicepack)) # Store name and tensor + loaded_voices.append( + (voice_file[:-3], voicepack) + ) # Store name and tensor # voicepack = torch.load(voice_path, map_location=TTSModel.get_device(), weights_only=True) # logger.info(f"Loaded voice {voice_file[:-3]} into cache") except Exception as e: logger.error(f"Failed to load voice {voice_file}: {e}") logger.info(f"Pre-loaded {len(loaded_voices)} voices into cache") return loaded_voices - - async def warmup_voices(self, warmup_text: str, loaded_voices: List[Tuple[str, torch.Tensor]]): + + async def warmup_voices( + self, warmup_text: str, loaded_voices: List[Tuple[str, torch.Tensor]] + ): """Warm up voice inference and streaming""" n_warmups = 1 for voice_name, _ in loaded_voices[:n_warmups]: try: logger.info(f"Running warmup inference on voice {voice_name}") async for _ in self.tts_service.generate_audio_stream( - warmup_text, - voice_name, - 1.0, - "pcm" + warmup_text, voice_name, 1.0, "pcm" ): pass # Process all chunks to properly warm up logger.info(f"Completed warmup for voice {voice_name}") diff --git a/api/src/structures/schemas.py b/api/src/structures/schemas.py index 48bc099..8db014c 100644 --- a/api/src/structures/schemas.py +++ b/api/src/structures/schemas.py @@ -1,14 +1,15 @@ from enum import Enum -from typing import Literal, Union, List +from typing import List, Union, Literal from pydantic import Field, BaseModel class VoiceCombineRequest(BaseModel): """Request schema for voice combination endpoint that accepts either a string with + or a list""" + voices: Union[str, List[str]] = Field( ..., - description="Either a string with voices separated by + (e.g. 'voice1+voice2') or a list of voice names to combine" + description="Either a string with voices separated by + (e.g. 'voice1+voice2') or a list of voice names to combine", ) diff --git a/api/src/structures/text_schemas.py b/api/src/structures/text_schemas.py index 27677c7..f820f68 100644 --- a/api/src/structures/text_schemas.py +++ b/api/src/structures/text_schemas.py @@ -1,14 +1,19 @@ -from pydantic import BaseModel, Field +from pydantic import Field, BaseModel + class PhonemeRequest(BaseModel): text: str language: str = "a" # Default to American English + class PhonemeResponse(BaseModel): phonemes: str tokens: list[int] + class GenerateFromPhonemesRequest(BaseModel): phonemes: str voice: str = Field(..., description="Voice ID to use for generation") - speed: float = Field(default=1.0, ge=0.1, le=5.0, description="Speed factor for generation") + speed: float = Field( + default=1.0, ge=0.1, le=5.0, description="Speed factor for generation" + ) diff --git a/api/tests/conftest.py b/api/tests/conftest.py index 0747ab5..6fdd9ea 100644 --- a/api/tests/conftest.py +++ b/api/tests/conftest.py @@ -1,9 +1,9 @@ import os import sys import shutil -from unittest.mock import Mock, patch, MagicMock -import numpy as np +from unittest.mock import Mock, MagicMock, patch +import numpy as np import pytest import aiofiles.threadpool @@ -37,6 +37,7 @@ mock_torch = Mock() mock_torch.cuda = Mock() mock_torch.cuda.is_available = Mock(return_value=False) + # Create a mock tensor class that supports basic operations class MockTensor: def __init__(self, data): @@ -46,54 +47,57 @@ class MockTensor: elif isinstance(data, MockTensor): self.shape = data.shape else: - self.shape = getattr(data, 'shape', [1]) - + self.shape = getattr(data, "shape", [1]) + def __getitem__(self, idx): if isinstance(self.data, (list, tuple)): if isinstance(idx, slice): return MockTensor(self.data[idx]) return self.data[idx] return self - + def max(self): if isinstance(self.data, (list, tuple)): max_val = max(self.data) return MockTensor(max_val) return 5 # Default for testing - + def item(self): if isinstance(self.data, (list, tuple)): return max(self.data) if isinstance(self.data, (int, float)): return self.data return 5 # Default for testing - + def cuda(self): """Support cuda conversion""" return self - + def any(self): if isinstance(self.data, (list, tuple)): return any(self.data) return False - + def all(self): if isinstance(self.data, (list, tuple)): return all(self.data) return True - + def unsqueeze(self, dim): return self - + def expand(self, *args): return self - + def type_as(self, other): return self + # Add tensor operations to mock torch mock_torch.tensor = lambda x: MockTensor(x) -mock_torch.zeros = lambda *args: MockTensor([0] * (args[0] if isinstance(args[0], int) else args[0][0])) +mock_torch.zeros = lambda *args: MockTensor( + [0] * (args[0] if isinstance(args[0], int) else args[0][0]) +) mock_torch.arange = lambda x: MockTensor(list(range(x))) mock_torch.gt = lambda x, y: MockTensor([False] * x.shape[0]) @@ -173,11 +177,13 @@ def mock_tts_service(monkeypatch): mock_service = Mock() mock_service._get_voice_path.return_value = "/mock/path/voice.pt" mock_service._load_voice.return_value = np.zeros((1, 192)) - + # Mock TTSModel.generate_from_tokens since we call it directly mock_generate = Mock(return_value=np.zeros(48000)) - monkeypatch.setattr("api.src.routers.text_processing.TTSModel.generate_from_tokens", mock_generate) - + monkeypatch.setattr( + "api.src.routers.text_processing.TTSModel.generate_from_tokens", mock_generate + ) + return mock_service diff --git a/api/tests/test_audio_service.py b/api/tests/test_audio_service.py index 540cd7f..6a22921 100644 --- a/api/tests/test_audio_service.py +++ b/api/tests/test_audio_service.py @@ -1,8 +1,9 @@ """Tests for AudioService""" +from unittest.mock import patch + import numpy as np import pytest -from unittest.mock import patch from api.src.services.audio import AudioService, AudioNormalizer @@ -10,10 +11,11 @@ from api.src.services.audio import AudioService, AudioNormalizer @pytest.fixture(autouse=True) def mock_settings(): """Mock settings for all tests""" - with patch('api.src.services.audio.settings') as mock_settings: + with patch("api.src.services.audio.settings") as mock_settings: mock_settings.gap_trim_ms = 250 yield mock_settings + @pytest.fixture def sample_audio(): """Generate a simple sine wave for testing""" diff --git a/api/tests/test_chunker.py b/api/tests/test_chunker.py index b96e8e7..002da72 100644 --- a/api/tests/test_chunker.py +++ b/api/tests/test_chunker.py @@ -1,14 +1,16 @@ """Tests for text chunking service""" -import pytest from unittest.mock import patch + +import pytest + from api.src.services.text_processing import chunker @pytest.fixture(autouse=True) def mock_settings(): """Mock settings for all tests""" - with patch('api.src.services.text_processing.chunker.settings') as mock_settings: + with patch("api.src.services.text_processing.chunker.settings") as mock_settings: mock_settings.max_chunk_size = 300 yield mock_settings diff --git a/api/tests/test_endpoints.py b/api/tests/test_endpoints.py index bd9e578..c3bcb43 100644 --- a/api/tests/test_endpoints.py +++ b/api/tests/test_endpoints.py @@ -1,16 +1,17 @@ +import asyncio from unittest.mock import Mock, AsyncMock import pytest import pytest_asyncio -import asyncio -from fastapi.testclient import TestClient from httpx import AsyncClient +from fastapi.testclient import TestClient from ..src.main import app # Create test client client = TestClient(app) + # Create async client fixture @pytest_asyncio.fixture async def async_client(): @@ -23,25 +24,28 @@ async def async_client(): def mock_tts_service(monkeypatch): mock_service = Mock() mock_service._generate_audio.return_value = (bytes([0, 1, 2, 3]), 1.0) - + # Create proper async generator mock async def mock_stream(*args, **kwargs): for chunk in [b"chunk1", b"chunk2"]: yield chunk + mock_service.generate_audio_stream = mock_stream - + # Create async mocks - mock_service.list_voices = AsyncMock(return_value=[ - "af", - "bm_lewis", - "bf_isabella", - "bf_emma", - "af_sarah", - "af_bella", - "am_adam", - "am_michael", - "bm_george", - ]) + mock_service.list_voices = AsyncMock( + return_value=[ + "af", + "bm_lewis", + "bf_isabella", + "bf_emma", + "af_sarah", + "af_bella", + "am_adam", + "am_michael", + "bm_george", + ] + ) mock_service.combine_voices = AsyncMock() monkeypatch.setattr( "api.src.routers.openai_compatible.TTSService", @@ -54,9 +58,7 @@ def mock_tts_service(monkeypatch): def mock_audio_service(monkeypatch): mock_service = Mock() mock_service.convert_audio.return_value = b"converted mock audio data" - monkeypatch.setattr( - "api.src.routers.openai_compatible.AudioService", mock_service - ) + monkeypatch.setattr("api.src.routers.openai_compatible.AudioService", mock_service) return mock_service @@ -68,7 +70,9 @@ def test_health_check(): @pytest.mark.asyncio -async def test_openai_speech_endpoint(mock_tts_service, mock_audio_service, async_client): +async def test_openai_speech_endpoint( + mock_tts_service, mock_audio_service, async_client +): """Test the OpenAI-compatible speech endpoint""" test_request = { "model": "kokoro", @@ -76,7 +80,7 @@ async def test_openai_speech_endpoint(mock_tts_service, mock_audio_service, asyn "voice": "bm_lewis", "response_format": "wav", "speed": 1.0, - "stream": False # Explicitly disable streaming + "stream": False, # Explicitly disable streaming } response = await async_client.post("/v1/audio/speech", json=test_request) assert response.status_code == 200 @@ -97,7 +101,7 @@ async def test_openai_speech_invalid_voice(mock_tts_service, async_client): "voice": "invalid_voice", "response_format": "wav", "speed": 1.0, - "stream": False # Explicitly disable streaming + "stream": False, # Explicitly disable streaming } response = await async_client.post("/v1/audio/speech", json=test_request) assert response.status_code == 400 # Bad request @@ -113,7 +117,7 @@ async def test_openai_speech_invalid_speed(mock_tts_service, async_client): "voice": "af", "response_format": "wav", "speed": -1.0, # Invalid speed - "stream": False # Explicitly disable streaming + "stream": False, # Explicitly disable streaming } response = await async_client.post("/v1/audio/speech", json=test_request) assert response.status_code == 422 # Validation error @@ -129,7 +133,7 @@ async def test_openai_speech_generation_error(mock_tts_service, async_client): "voice": "af", "response_format": "wav", "speed": 1.0, - "stream": False # Explicitly disable streaming + "stream": False, # Explicitly disable streaming } response = await async_client.post("/v1/audio/speech", json=test_request) assert response.status_code == 500 @@ -159,7 +163,9 @@ async def test_combine_voices_string_success(mock_tts_service, async_client): assert response.status_code == 200 assert response.json()["voice"] == "af_bella_af_sarah" - mock_tts_service.combine_voices.assert_called_once_with(voices=["af_bella", "af_sarah"]) + mock_tts_service.combine_voices.assert_called_once_with( + voices=["af_bella", "af_sarah"] + ) @pytest.mark.asyncio @@ -184,7 +190,9 @@ async def test_combine_voices_empty_list(mock_tts_service, async_client): async def test_combine_voices_error(mock_tts_service, async_client): """Test error handling in voice combination""" test_voices = ["af_bella", "af_sarah"] - mock_tts_service.combine_voices = AsyncMock(side_effect=Exception("Combination failed")) + mock_tts_service.combine_voices = AsyncMock( + side_effect=Exception("Combination failed") + ) response = await async_client.post("/v1/audio/voices/combine", json=test_voices) assert response.status_code == 500 @@ -192,50 +200,56 @@ async def test_combine_voices_error(mock_tts_service, async_client): @pytest.mark.asyncio -async def test_speech_with_combined_voice(mock_tts_service, mock_audio_service, async_client): +async def test_speech_with_combined_voice( + mock_tts_service, mock_audio_service, async_client +): """Test speech generation with combined voice using + syntax""" mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - + test_request = { "model": "kokoro", "input": "Hello world", "voice": "af_bella+af_sarah", "response_format": "wav", "speed": 1.0, - "stream": False + "stream": False, } - + response = await async_client.post("/v1/audio/speech", json=test_request) - + assert response.status_code == 200 assert response.headers["content-type"] == "audio/wav" mock_tts_service._generate_audio.assert_called_once_with( - text="Hello world", - voice="af_bella_af_sarah", - speed=1.0, - stitch_long_output=True + text="Hello world", + voice="af_bella_af_sarah", + speed=1.0, + stitch_long_output=True, ) @pytest.mark.asyncio -async def test_speech_with_whitespace_in_voice(mock_tts_service, mock_audio_service, async_client): +async def test_speech_with_whitespace_in_voice( + mock_tts_service, mock_audio_service, async_client +): """Test speech generation with whitespace in voice combination""" mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - + test_request = { "model": "kokoro", "input": "Hello world", "voice": " af_bella + af_sarah ", "response_format": "wav", "speed": 1.0, - "stream": False + "stream": False, } - + response = await async_client.post("/v1/audio/speech", json=test_request) - + assert response.status_code == 200 assert response.headers["content-type"] == "audio/wav" - mock_tts_service.combine_voices.assert_called_once_with(voices=["af_bella", "af_sarah"]) + mock_tts_service.combine_voices.assert_called_once_with( + voices=["af_bella", "af_sarah"] + ) @pytest.mark.asyncio @@ -247,9 +261,9 @@ async def test_speech_with_empty_voice_combination(mock_tts_service, async_clien "voice": "+", "response_format": "wav", "speed": 1.0, - "stream": False + "stream": False, } - + response = await async_client.post("/v1/audio/speech", json=test_request) assert response.status_code == 400 assert "No voices provided" in response.json()["detail"]["message"] @@ -264,9 +278,9 @@ async def test_speech_with_invalid_combined_voice(mock_tts_service, async_client "voice": "invalid+combination", "response_format": "wav", "speed": 1.0, - "stream": False + "stream": False, } - + response = await async_client.post("/v1/audio/speech", json=test_request) assert response.status_code == 400 assert "not found" in response.json()["detail"]["message"] @@ -276,25 +290,28 @@ async def test_speech_with_invalid_combined_voice(mock_tts_service, async_client async def test_speech_streaming_with_combined_voice(mock_tts_service, async_client): """Test streaming speech with combined voice using + syntax""" mock_tts_service.combine_voices = AsyncMock(return_value="af_bella_af_sarah") - + test_request = { "model": "kokoro", "input": "Hello world", "voice": "af_bella+af_sarah", "response_format": "mp3", - "stream": True + "stream": True, } - + # Create streaming mock async def mock_stream(*args, **kwargs): for chunk in [b"mp3header", b"mp3data"]: yield chunk + mock_tts_service.generate_audio_stream = mock_stream - + # Add streaming header headers = {"x-raw-response": "stream"} - response = await async_client.post("/v1/audio/speech", json=test_request, headers=headers) - + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + assert response.status_code == 200 assert response.headers["content-type"] == "audio/mpeg" assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" @@ -308,19 +325,22 @@ async def test_openai_speech_pcm_streaming(mock_tts_service, async_client): "input": "Hello world", "voice": "af", "response_format": "pcm", - "stream": True + "stream": True, } - + # Create streaming mock for this test async def mock_stream(*args, **kwargs): for chunk in [b"chunk1", b"chunk2"]: yield chunk + mock_tts_service.generate_audio_stream = mock_stream - + # Add streaming header headers = {"x-raw-response": "stream"} - response = await async_client.post("/v1/audio/speech", json=test_request, headers=headers) - + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + assert response.status_code == 200 assert response.headers["content-type"] == "audio/pcm" @@ -333,19 +353,22 @@ async def test_openai_speech_streaming_mp3(mock_tts_service, async_client): "input": "Hello world", "voice": "af", "response_format": "mp3", - "stream": True + "stream": True, } - + # Create streaming mock for this test async def mock_stream(*args, **kwargs): for chunk in [b"mp3header", b"mp3data"]: yield chunk + mock_tts_service.generate_audio_stream = mock_stream - + # Add streaming header headers = {"x-raw-response": "stream"} - response = await async_client.post("/v1/audio/speech", json=test_request, headers=headers) - + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + assert response.status_code == 200 assert response.headers["content-type"] == "audio/mpeg" assert response.headers["content-disposition"] == "attachment; filename=speech.mp3" @@ -359,18 +382,21 @@ async def test_openai_speech_streaming_generator(mock_tts_service, async_client) "input": "Hello world", "voice": "af", "response_format": "pcm", - "stream": True + "stream": True, } - + # Create streaming mock for this test async def mock_stream(*args, **kwargs): for chunk in [b"chunk1", b"chunk2"]: yield chunk + mock_tts_service.generate_audio_stream = mock_stream - + # Add streaming header headers = {"x-raw-response": "stream"} - response = await async_client.post("/v1/audio/speech", json=test_request, headers=headers) - + response = await async_client.post( + "/v1/audio/speech", json=test_request, headers=headers + ) + assert response.status_code == 200 assert response.headers["content-type"] == "audio/pcm" diff --git a/api/tests/test_main.py b/api/tests/test_main.py index cb7aa8b..f779483 100644 --- a/api/tests/test_main.py +++ b/api/tests/test_main.py @@ -1,6 +1,6 @@ """Tests for FastAPI application""" -from unittest.mock import MagicMock, patch, call +from unittest.mock import MagicMock, call, patch import pytest from fastapi.testclient import TestClient @@ -28,14 +28,15 @@ async def test_lifespan_successful_warmup(mock_logger, mock_tts_model): """Test successful model warmup in lifespan""" # Mock file system for voice counting mock_tts_model.VOICES_DIR = "/mock/voices" - + # Create async mock async def async_setup(): return 3 + mock_tts_model.setup = MagicMock() mock_tts_model.setup.side_effect = async_setup mock_tts_model.get_device.return_value = "cuda" - + with patch("os.listdir", return_value=["voice1.pt", "voice2.pt", "voice3.pt"]): # Create an async generator from the lifespan context manager async_gen = lifespan(MagicMock()) @@ -44,7 +45,7 @@ async def test_lifespan_successful_warmup(mock_logger, mock_tts_model): # Verify the expected logging sequence mock_logger.info.assert_any_call("Loading TTS model and voice packs...") - + # Check for the startup message containing the required info startup_calls = [call[0][0] for call in mock_logger.info.call_args_list] startup_msg = next(msg for msg in startup_calls if "Model warmed up on" in msg) @@ -86,14 +87,15 @@ async def test_lifespan_cuda_warmup(mock_tts_model): """Test model warmup specifically on CUDA""" # Mock file system for voice counting mock_tts_model.VOICES_DIR = "/mock/voices" - + # Create async mock async def async_setup(): return 2 + mock_tts_model.setup = MagicMock() mock_tts_model.setup.side_effect = async_setup mock_tts_model.get_device.return_value = "cuda" - + with patch("os.listdir", return_value=["voice1.pt", "voice2.pt"]): # Create an async generator from the lifespan context manager async_gen = lifespan(MagicMock()) diff --git a/api/tests/test_normalizer.py b/api/tests/test_normalizer.py index 9555e22..9146252 100644 --- a/api/tests/test_normalizer.py +++ b/api/tests/test_normalizer.py @@ -1,43 +1,88 @@ """Tests for text normalization service""" import pytest + from api.src.services.text_processing.normalizer import normalize_text + def test_url_protocols(): """Test URL protocol handling""" - assert normalize_text("Check out https://example.com") == "Check out https example dot com" + assert ( + normalize_text("Check out https://example.com") + == "Check out https example dot com" + ) assert normalize_text("Visit http://site.com") == "Visit http site dot com" - assert normalize_text("Go to https://test.org/path") == "Go to https test dot org slash path" + assert ( + normalize_text("Go to https://test.org/path") + == "Go to https test dot org slash path" + ) + def test_url_www(): """Test www prefix handling""" assert normalize_text("Go to www.example.com") == "Go to www example dot com" - assert normalize_text("Visit www.test.org/docs") == "Visit www test dot org slash docs" - assert normalize_text("Check www.site.com?q=test") == "Check www site dot com question-mark q equals test" + assert ( + normalize_text("Visit www.test.org/docs") == "Visit www test dot org slash docs" + ) + assert ( + normalize_text("Check www.site.com?q=test") + == "Check www site dot com question-mark q equals test" + ) + def test_url_localhost(): """Test localhost URL handling""" - assert normalize_text("Running on localhost:7860") == "Running on localhost colon 78 60" - assert normalize_text("Server at localhost:8080/api") == "Server at localhost colon 80 80 slash api" - assert normalize_text("Test localhost:3000/test?v=1") == "Test localhost colon 3000 slash test question-mark v equals 1" + assert ( + normalize_text("Running on localhost:7860") + == "Running on localhost colon 78 60" + ) + assert ( + normalize_text("Server at localhost:8080/api") + == "Server at localhost colon 80 80 slash api" + ) + assert ( + normalize_text("Test localhost:3000/test?v=1") + == "Test localhost colon 3000 slash test question-mark v equals 1" + ) + def test_url_ip_addresses(): """Test IP address URL handling""" - assert normalize_text("Access 0.0.0.0:9090/test") == "Access 0 dot 0 dot 0 dot 0 colon 90 90 slash test" - assert normalize_text("API at 192.168.1.1:8000") == "API at 192 dot 168 dot 1 dot 1 colon 8000" + assert ( + normalize_text("Access 0.0.0.0:9090/test") + == "Access 0 dot 0 dot 0 dot 0 colon 90 90 slash test" + ) + assert ( + normalize_text("API at 192.168.1.1:8000") + == "API at 192 dot 168 dot 1 dot 1 colon 8000" + ) assert normalize_text("Server 127.0.0.1") == "Server 127 dot 0 dot 0 dot 1" + def test_url_raw_domains(): """Test raw domain handling""" - assert normalize_text("Visit google.com/search") == "Visit google dot com slash search" - assert normalize_text("Go to example.com/path?q=test") == "Go to example dot com slash path question-mark q equals test" + assert ( + normalize_text("Visit google.com/search") == "Visit google dot com slash search" + ) + assert ( + normalize_text("Go to example.com/path?q=test") + == "Go to example dot com slash path question-mark q equals test" + ) assert normalize_text("Check docs.test.com") == "Check docs dot test dot com" + def test_url_email_addresses(): """Test email address handling""" - assert normalize_text("Email me at user@example.com") == "Email me at user at example dot com" + assert ( + normalize_text("Email me at user@example.com") + == "Email me at user at example dot com" + ) assert normalize_text("Contact admin@test.org") == "Contact admin at test dot org" - assert normalize_text("Send to test.user@site.com") == "Send to test dot user at site dot com" + assert ( + normalize_text("Send to test.user@site.com") + == "Send to test dot user at site dot com" + ) + def test_non_url_text(): """Test that non-URL text is unaffected""" diff --git a/api/tests/test_text_processing.py b/api/tests/test_text_processing.py index 931e362..e5d65df 100644 --- a/api/tests/test_text_processing.py +++ b/api/tests/test_text_processing.py @@ -1,80 +1,92 @@ """Tests for text processing endpoints""" + from unittest.mock import Mock, patch + +import numpy as np import pytest import pytest_asyncio from httpx import AsyncClient -import numpy as np -from ..src.main import app from .conftest import MockTTSModel +from ..src.main import app + @pytest_asyncio.fixture async def async_client(): async with AsyncClient(app=app, base_url="http://test") as ac: yield ac + @pytest.mark.asyncio async def test_phonemize_endpoint(async_client): """Test phoneme generation endpoint""" - with patch('api.src.routers.text_processing.phonemize') as mock_phonemize, \ - patch('api.src.routers.text_processing.tokenize') as mock_tokenize: - + with patch("api.src.routers.text_processing.phonemize") as mock_phonemize, patch( + "api.src.routers.text_processing.tokenize" + ) as mock_tokenize: # Setup mocks mock_phonemize.return_value = "həlˈoʊ" mock_tokenize.return_value = [1, 2, 3] - + # Test request - response = await async_client.post("/text/phonemize", json={ - "text": "hello", - "language": "a" - }) - + response = await async_client.post( + "/text/phonemize", json={"text": "hello", "language": "a"} + ) + # Verify response assert response.status_code == 200 result = response.json() assert result["phonemes"] == "həlˈoʊ" assert result["tokens"] == [0, 1, 2, 3, 0] # Should add start/end tokens + @pytest.mark.asyncio async def test_phonemize_empty_text(async_client): """Test phoneme generation with empty text""" - response = await async_client.post("/text/phonemize", json={ - "text": "", - "language": "a" - }) - + response = await async_client.post( + "/text/phonemize", json={"text": "", "language": "a"} + ) + assert response.status_code == 500 assert "error" in response.json()["detail"] + @pytest.mark.asyncio -async def test_generate_from_phonemes(async_client, mock_tts_service, mock_audio_service): +async def test_generate_from_phonemes( + async_client, mock_tts_service, mock_audio_service +): """Test audio generation from phonemes""" - with patch('api.src.routers.text_processing.TTSService', return_value=mock_tts_service): - response = await async_client.post("/text/generate_from_phonemes", json={ - "phonemes": "həlˈoʊ", - "voice": "af_bella", - "speed": 1.0 - }) - + with patch( + "api.src.routers.text_processing.TTSService", return_value=mock_tts_service + ): + response = await async_client.post( + "/text/generate_from_phonemes", + json={"phonemes": "həlˈoʊ", "voice": "af_bella", "speed": 1.0}, + ) + assert response.status_code == 200 assert response.headers["content-type"] == "audio/wav" - assert response.headers["content-disposition"] == "attachment; filename=speech.wav" + assert ( + response.headers["content-disposition"] == "attachment; filename=speech.wav" + ) assert response.content == b"mock audio data" + @pytest.mark.asyncio async def test_generate_from_phonemes_invalid_voice(async_client, mock_tts_service): """Test audio generation with invalid voice""" mock_tts_service._get_voice_path.return_value = None - with patch('api.src.routers.text_processing.TTSService', return_value=mock_tts_service): - response = await async_client.post("/text/generate_from_phonemes", json={ - "phonemes": "həlˈoʊ", - "voice": "invalid_voice", - "speed": 1.0 - }) - + with patch( + "api.src.routers.text_processing.TTSService", return_value=mock_tts_service + ): + response = await async_client.post( + "/text/generate_from_phonemes", + json={"phonemes": "həlˈoʊ", "voice": "invalid_voice", "speed": 1.0}, + ) + assert response.status_code == 400 assert "Voice not found" in response.json()["detail"]["message"] + @pytest.mark.asyncio async def test_generate_from_phonemes_invalid_speed(async_client, monkeypatch): """Test audio generation with invalid speed""" @@ -82,25 +94,29 @@ async def test_generate_from_phonemes_invalid_speed(async_client, monkeypatch): mock_model = Mock() mock_model.generate_from_tokens = Mock(return_value=np.zeros(48000)) monkeypatch.setattr("api.src.services.tts_model.TTSModel._instance", mock_model) - monkeypatch.setattr("api.src.services.tts_model.TTSModel.get_instance", Mock(return_value=mock_model)) - - response = await async_client.post("/text/generate_from_phonemes", json={ - "phonemes": "həlˈoʊ", - "voice": "af_bella", - "speed": -1.0 - }) - + monkeypatch.setattr( + "api.src.services.tts_model.TTSModel.get_instance", + Mock(return_value=mock_model), + ) + + response = await async_client.post( + "/text/generate_from_phonemes", + json={"phonemes": "həlˈoʊ", "voice": "af_bella", "speed": -1.0}, + ) + assert response.status_code == 422 # Validation error + @pytest.mark.asyncio async def test_generate_from_phonemes_empty_phonemes(async_client, mock_tts_service): """Test audio generation with empty phonemes""" - with patch('api.src.routers.text_processing.TTSService', return_value=mock_tts_service): - response = await async_client.post("/text/generate_from_phonemes", json={ - "phonemes": "", - "voice": "af_bella", - "speed": 1.0 - }) - + with patch( + "api.src.routers.text_processing.TTSService", return_value=mock_tts_service + ): + response = await async_client.post( + "/text/generate_from_phonemes", + json={"phonemes": "", "voice": "af_bella", "speed": 1.0}, + ) + assert response.status_code == 400 assert "Invalid request" in response.json()["detail"]["error"] diff --git a/api/tests/test_tts_implementations.py b/api/tests/test_tts_implementations.py index 2844f33..99b28bf 100644 --- a/api/tests/test_tts_implementations.py +++ b/api/tests/test_tts_implementations.py @@ -1,13 +1,16 @@ """Tests for TTS model implementations""" + import os +from unittest.mock import MagicMock, patch + +import numpy as np import torch import pytest -import numpy as np -from unittest.mock import patch, MagicMock -from api.src.services.tts_base import TTSBaseModel from api.src.services.tts_cpu import TTSCPUModel from api.src.services.tts_gpu import TTSGPUModel, length_to_mask +from api.src.services.tts_base import TTSBaseModel + # Base Model Tests def test_get_device_error(): @@ -16,14 +19,17 @@ def test_get_device_error(): with pytest.raises(RuntimeError, match="Model not initialized"): TTSBaseModel.get_device() + @pytest.mark.asyncio -@patch('torch.cuda.is_available') -@patch('os.path.exists') -@patch('os.path.join') -@patch('os.listdir') -@patch('torch.load') -@patch('torch.save') -async def test_setup_cuda_available(mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available): +@patch("torch.cuda.is_available") +@patch("os.path.exists") +@patch("os.path.join") +@patch("os.listdir") +@patch("torch.load") +@patch("torch.save") +async def test_setup_cuda_available( + mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available +): """Test setup with CUDA available""" TTSBaseModel._device = None mock_cuda_available.return_value = True @@ -31,29 +37,32 @@ async def test_setup_cuda_available(mock_save, mock_load, mock_listdir, mock_joi mock_load.return_value = torch.zeros(1) mock_listdir.return_value = ["voice1.pt", "voice2.pt"] mock_join.return_value = "/mocked/path" - + # Create mock model mock_model = MagicMock() mock_model.bert = MagicMock() - mock_model.process_text = MagicMock(return_value=("dummy", [1,2,3])) + mock_model.process_text = MagicMock(return_value=("dummy", [1, 2, 3])) mock_model.generate_from_tokens = MagicMock(return_value=np.zeros(1000)) - + # Mock initialize to return our mock model TTSBaseModel.initialize = MagicMock(return_value=mock_model) TTSBaseModel._instance = mock_model - + voice_count = await TTSBaseModel.setup() assert TTSBaseModel._device == "cuda" assert voice_count == 2 + @pytest.mark.asyncio -@patch('torch.cuda.is_available') -@patch('os.path.exists') -@patch('os.path.join') -@patch('os.listdir') -@patch('torch.load') -@patch('torch.save') -async def test_setup_cuda_unavailable(mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available): +@patch("torch.cuda.is_available") +@patch("os.path.exists") +@patch("os.path.join") +@patch("os.listdir") +@patch("torch.load") +@patch("torch.save") +async def test_setup_cuda_unavailable( + mock_save, mock_load, mock_listdir, mock_join, mock_exists, mock_cuda_available +): """Test setup with CUDA unavailable""" TTSBaseModel._device = None mock_cuda_available.return_value = False @@ -61,98 +70,105 @@ async def test_setup_cuda_unavailable(mock_save, mock_load, mock_listdir, mock_j mock_load.return_value = torch.zeros(1) mock_listdir.return_value = ["voice1.pt", "voice2.pt"] mock_join.return_value = "/mocked/path" - + # Create mock model mock_model = MagicMock() mock_model.bert = MagicMock() - mock_model.process_text = MagicMock(return_value=("dummy", [1,2,3])) + mock_model.process_text = MagicMock(return_value=("dummy", [1, 2, 3])) mock_model.generate_from_tokens = MagicMock(return_value=np.zeros(1000)) - + # Mock initialize to return our mock model TTSBaseModel.initialize = MagicMock(return_value=mock_model) TTSBaseModel._instance = mock_model - + voice_count = await TTSBaseModel.setup() assert TTSBaseModel._device == "cpu" assert voice_count == 2 + # CPU Model Tests def test_cpu_initialize_missing_model(): """Test CPU initialize with missing model""" TTSCPUModel._onnx_session = None # Reset the session - with patch('os.path.exists', return_value=False), \ - patch('onnxruntime.InferenceSession', return_value=None): + with patch("os.path.exists", return_value=False), patch( + "onnxruntime.InferenceSession", return_value=None + ): result = TTSCPUModel.initialize("dummy_dir") assert result is None + def test_cpu_generate_uninitialized(): """Test CPU generate methods with uninitialized model""" TTSCPUModel._onnx_session = None - + with pytest.raises(RuntimeError, match="ONNX model not initialized"): TTSCPUModel.generate_from_text("test", torch.zeros(1), "en", 1.0) - + with pytest.raises(RuntimeError, match="ONNX model not initialized"): - TTSCPUModel.generate_from_tokens([1,2,3], torch.zeros(1), 1.0) + TTSCPUModel.generate_from_tokens([1, 2, 3], torch.zeros(1), 1.0) + def test_cpu_process_text(): """Test CPU process_text functionality""" - with patch('api.src.services.tts_cpu.phonemize') as mock_phonemize, \ - patch('api.src.services.tts_cpu.tokenize') as mock_tokenize: - + with patch("api.src.services.tts_cpu.phonemize") as mock_phonemize, patch( + "api.src.services.tts_cpu.tokenize" + ) as mock_tokenize: mock_phonemize.return_value = "test phonemes" mock_tokenize.return_value = [1, 2, 3] - + phonemes, tokens = TTSCPUModel.process_text("test", "en") assert phonemes == "test phonemes" assert tokens == [0, 1, 2, 3, 0] # Should add start/end tokens + # GPU Model Tests -@patch('torch.cuda.is_available') +@patch("torch.cuda.is_available") def test_gpu_initialize_cuda_unavailable(mock_cuda_available): """Test GPU initialize with CUDA unavailable""" mock_cuda_available.return_value = False TTSGPUModel._instance = None - + result = TTSGPUModel.initialize("dummy_dir", "dummy_path") assert result is None -@patch('api.src.services.tts_gpu.length_to_mask') + +@patch("api.src.services.tts_gpu.length_to_mask") def test_gpu_length_to_mask(mock_length_to_mask): """Test length_to_mask function""" # Setup mock return value - expected_mask = torch.tensor([ - [False, False, False, True, True], - [False, False, False, False, False] - ]) + expected_mask = torch.tensor( + [[False, False, False, True, True], [False, False, False, False, False]] + ) mock_length_to_mask.return_value = expected_mask - + # Call function with test input lengths = torch.tensor([3, 5]) mask = mock_length_to_mask(lengths) - + # Verify mock was called with correct input mock_length_to_mask.assert_called_once() assert torch.equal(mask, expected_mask) + def test_gpu_generate_uninitialized(): """Test GPU generate methods with uninitialized model""" TTSGPUModel._instance = None - + with pytest.raises(RuntimeError, match="GPU model not initialized"): TTSGPUModel.generate_from_text("test", torch.zeros(1), "en", 1.0) - + with pytest.raises(RuntimeError, match="GPU model not initialized"): - TTSGPUModel.generate_from_tokens([1,2,3], torch.zeros(1), 1.0) + TTSGPUModel.generate_from_tokens([1, 2, 3], torch.zeros(1), 1.0) + def test_gpu_process_text(): """Test GPU process_text functionality""" - with patch('api.src.services.tts_gpu.phonemize') as mock_phonemize, \ - patch('api.src.services.tts_gpu.tokenize') as mock_tokenize: - + with patch("api.src.services.tts_gpu.phonemize") as mock_phonemize, patch( + "api.src.services.tts_gpu.tokenize" + ) as mock_tokenize: mock_phonemize.return_value = "test phonemes" mock_tokenize.return_value = [1, 2, 3] - + phonemes, tokens = TTSGPUModel.process_text("test", "en") assert phonemes == "test phonemes" assert tokens == [1, 2, 3] # GPU implementation doesn't add start/end tokens diff --git a/api/tests/test_tts_service.py b/api/tests/test_tts_service.py index c37c644..0f613da 100644 --- a/api/tests/test_tts_service.py +++ b/api/tests/test_tts_service.py @@ -9,10 +9,10 @@ import pytest from onnxruntime import InferenceSession from api.src.core.config import settings -from api.src.services.tts_model import TTSModel -from api.src.services.tts_service import TTSService from api.src.services.tts_cpu import TTSCPUModel from api.src.services.tts_gpu import TTSGPUModel +from api.src.services.tts_model import TTSModel +from api.src.services.tts_service import TTSService @pytest.fixture @@ -22,12 +22,17 @@ def tts_service(monkeypatch): mock_model = MagicMock() mock_model.generate_from_tokens = MagicMock(return_value=np.zeros(48000)) mock_model.process_text = MagicMock(return_value=("mock phonemes", [1, 2, 3])) - + # Set up model instance monkeypatch.setattr("api.src.services.tts_model.TTSModel._instance", mock_model) - monkeypatch.setattr("api.src.services.tts_model.TTSModel.get_instance", MagicMock(return_value=mock_model)) - monkeypatch.setattr("api.src.services.tts_model.TTSModel.get_device", MagicMock(return_value="cpu")) - + monkeypatch.setattr( + "api.src.services.tts_model.TTSModel.get_instance", + MagicMock(return_value=mock_model), + ) + monkeypatch.setattr( + "api.src.services.tts_model.TTSModel.get_device", MagicMock(return_value="cpu") + ) + return TTSService() @@ -51,13 +56,15 @@ def test_audio_to_bytes(tts_service, sample_audio): @pytest.mark.asyncio async def test_list_voices(tts_service): """Test listing available voices""" - # Override list_voices for testing - # # TODO: + + # Override list_voices for testing + # # TODO: # Whatever aiofiles does here pathing aiofiles vs aiofiles.os - # I am thoroughly confused by it. + # I am thoroughly confused by it. # Cheating the test as it seems to work in the real world (for now) async def mock_list_voices(): return ["voice1", "voice2"] + tts_service.list_voices = mock_list_voices voices = await tts_service.list_voices() @@ -69,10 +76,12 @@ async def test_list_voices(tts_service): @pytest.mark.asyncio async def test_list_voices_error(tts_service): """Test error handling in list_voices""" + # Override list_voices for testing # TODO: See above. async def mock_list_voices(): return [] + tts_service.list_voices = mock_list_voices voices = await tts_service.list_voices() @@ -93,7 +102,7 @@ def mock_model_setup(cuda_available=False): # Set device based on CUDA availability TTSModel._device = "cuda" if cuda_available else "cpu" - + return 3 # Return voice count (including af.pt) @@ -101,7 +110,7 @@ def test_model_initialization_cuda(): """Test model initialization with CUDA""" # Simulate CUDA availability voice_count = mock_model_setup(cuda_available=True) - + assert TTSModel.get_device() == "cuda" assert voice_count == 3 # voice1.pt, voice2.pt, af.pt @@ -110,7 +119,7 @@ def test_model_initialization_cpu(): """Test model initialization with CPU""" # Simulate no CUDA availability voice_count = mock_model_setup(cuda_available=False) - + assert TTSModel.get_device() == "cpu" assert voice_count == 3 # voice1.pt, voice2.pt, af.pt @@ -124,10 +133,11 @@ def test_generate_audio_empty_text(tts_service): @pytest.fixture(autouse=True) def mock_settings(): """Mock settings for all tests""" - with patch('api.src.services.text_processing.chunker.settings') as mock_settings: + with patch("api.src.services.text_processing.chunker.settings") as mock_settings: mock_settings.max_chunk_size = 300 yield mock_settings + @patch("api.src.services.tts_model.TTSModel.get_instance") @patch("api.src.services.tts_model.TTSModel.get_device") @patch("os.path.exists") @@ -150,7 +160,10 @@ def test_generate_audio_phonemize_error( """Test handling phonemization error""" mock_normalize.return_value = "Test text" mock_phonemize.side_effect = Exception("Phonemization failed") - mock_instance.return_value = (mock_generate, "cpu") # Use the same mock for consistency + mock_instance.return_value = ( + mock_generate, + "cpu", + ) # Use the same mock for consistency mock_get_device.return_value = "cpu" mock_exists.return_value = True mock_torch_load.return_value = torch.zeros((10, 24000)) @@ -184,7 +197,10 @@ def test_generate_audio_error( mock_phonemize.return_value = "Test text" mock_tokenize.return_value = [1, 2] # Return integers instead of strings mock_generate.side_effect = Exception("Generation failed") - mock_instance.return_value = (mock_generate, "cpu") # Use the same mock for consistency + mock_instance.return_value = ( + mock_generate, + "cpu", + ) # Use the same mock for consistency mock_get_device.return_value = "cpu" mock_exists.return_value = True mock_torch_load.return_value = torch.zeros((10, 24000)) @@ -205,12 +221,11 @@ def test_save_audio(tts_service, sample_audio, tmp_path): async def test_combine_voices(tts_service): """Test combining multiple voices""" # Setup mocks for torch operations - with patch('torch.load', return_value=torch.tensor([1.0, 2.0])), \ - patch('torch.stack', return_value=torch.tensor([[1.0, 2.0], [3.0, 4.0]])), \ - patch('torch.mean', return_value=torch.tensor([2.0, 3.0])), \ - patch('torch.save'), \ - patch('os.path.exists', return_value=True): - + with patch("torch.load", return_value=torch.tensor([1.0, 2.0])), patch( + "torch.stack", return_value=torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + ), patch("torch.mean", return_value=torch.tensor([2.0, 3.0])), patch( + "torch.save" + ), patch("os.path.exists", return_value=True): # Test combining two voices result = await tts_service.combine_voices(["voice1", "voice2"]) diff --git a/examples/assorted_checks/benchmarks/benchmark_first_token_stream_unified.py b/examples/assorted_checks/benchmarks/benchmark_first_token_stream_unified.py index 0b673ae..df1855c 100644 --- a/examples/assorted_checks/benchmarks/benchmark_first_token_stream_unified.py +++ b/examples/assorted_checks/benchmarks/benchmark_first_token_stream_unified.py @@ -166,7 +166,7 @@ def measure_first_token_openai( def main(): script_dir = os.path.dirname(os.path.abspath(__file__)) - prefix='cpu' + prefix = "cpu" # Run requests benchmark print("\n=== Running Direct Requests Benchmark ===") run_benchmark( @@ -176,7 +176,7 @@ def main(): output_plots_dir=os.path.join(script_dir, "output_plots"), suffix="_stream", plot_title_suffix="(Streaming)", - prefix=prefix + prefix=prefix, ) # Run OpenAI benchmark print("\n=== Running OpenAI Library Benchmark ===") @@ -187,7 +187,7 @@ def main(): output_plots_dir=os.path.join(script_dir, "output_plots"), suffix="_stream_openai", plot_title_suffix="(OpenAI Streaming)", - prefix=prefix + prefix=prefix, ) diff --git a/examples/assorted_checks/benchmarks/lib/stream_utils.py b/examples/assorted_checks/benchmarks/lib/stream_utils.py index 623b18a..d2decec 100644 --- a/examples/assorted_checks/benchmarks/lib/stream_utils.py +++ b/examples/assorted_checks/benchmarks/lib/stream_utils.py @@ -149,19 +149,19 @@ def run_benchmark( result["run_number"] = i + 1 # Handle time to first audio - first_chunk = result.get('time_to_first_chunk') + first_chunk = result.get("time_to_first_chunk") print( f"Time to First Audio: {f'{first_chunk:.3f}s' if first_chunk is not None else 'N/A'}" ) - + # Handle total time - total_time = result.get('total_time') + total_time = result.get("total_time") print( f"Time to Save Complete: {f'{total_time:.3f}s' if total_time is not None else 'N/A'}" ) - + # Handle audio length - audio_length = result.get('audio_length') + audio_length = result.get("audio_length") print( f"Audio length: {f'{audio_length:.3f}s' if audio_length is not None else 'N/A'}" ) @@ -191,10 +191,18 @@ def run_benchmark( # Print paths print("\nResults and plots saved to:") - print(f"- {os.path.join(output_data_dir, f'{prefix}first_token_benchmark{suffix}.json')}") - print(f"- {os.path.join(output_plots_dir, f'{prefix}first_token_latency{suffix}.png')}") - print(f"- {os.path.join(output_plots_dir, f'{prefix}total_time_latency{suffix}.png')}") - print(f"- {os.path.join(output_plots_dir, f'{prefix}first_token_timeline{suffix}.png')}") + print( + f"- {os.path.join(output_data_dir, f'{prefix}first_token_benchmark{suffix}.json')}" + ) + print( + f"- {os.path.join(output_plots_dir, f'{prefix}first_token_latency{suffix}.png')}" + ) + print( + f"- {os.path.join(output_plots_dir, f'{prefix}total_time_latency{suffix}.png')}" + ) + print( + f"- {os.path.join(output_plots_dir, f'{prefix}first_token_timeline{suffix}.png')}" + ) # Print silence check summary if silent_files: diff --git a/examples/openai_streaming_audio.py b/examples/openai_streaming_audio.py index 888417c..9e80bbd 100644 --- a/examples/openai_streaming_audio.py +++ b/examples/openai_streaming_audio.py @@ -1,42 +1,39 @@ - #!/usr/bin/env rye run python - +# %% import time from pathlib import Path from openai import OpenAI # gets OPENAI_API_KEY from your environment variables -openai = OpenAI(base_url="http://localhost:8880/v1", api_key="not-needed-for-local") +openai = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed-for-local") speech_file_path = Path(__file__).parent / "speech.mp3" + + + + def main() -> None: stream_to_speakers() - # Create text-to-speech audio file - with openai.audio.speech.with_streaming_response.create( - model="kokoro", - voice="af", - input="the quick brown fox jumped over the lazy dogs", - ) as response: - response.stream_to_file(speech_file_path) - def stream_to_speakers() -> None: import pyaudio - player_stream = pyaudio.PyAudio().open(format=pyaudio.paInt16, channels=1, rate=24000, output=True) + player_stream = pyaudio.PyAudio().open( + format=pyaudio.paInt16, channels=1, rate=24000, output=True + ) start_time = time.time() with openai.audio.speech.with_streaming_response.create( model="kokoro", - voice="af_0p0_n2p0", - response_format="pcm", # similar to WAV, but without a header chunk at the start. - input="""My dear sir, that is just where you are wrong. That is just where the whole world has gone wrong. We are always getting away from the present moment. Our mental existences, which are immaterial and have no dimensions, are passing along the Time-Dimension with a uniform velocity from the cradle to the grave. Just as we should travel down if we began our existence fifty miles above the earth’s surface""", + voice=VOICE, + response_format="mp3", # similar to WAV, but without a header chunk at the start. + input="""My dear sir, that is just where you are wrong. That is just where the whole world has gone wrong. We are always getting away from the present moment. Our mental existences, which are immaterial and have no dimensions, are passing along the Time-Dimension""", ) as response: print(f"Time to first byte: {int((time.time() - start_time) * 1000)}ms") for chunk in response.iter_bytes(chunk_size=1024): @@ -47,3 +44,5 @@ def stream_to_speakers() -> None: if __name__ == "__main__": main() + +# %% diff --git a/examples/phoneme_examples/generate_phonemes.py b/examples/phoneme_examples/generate_phonemes.py index 416c566..6b261a8 100644 --- a/examples/phoneme_examples/generate_phonemes.py +++ b/examples/phoneme_examples/generate_phonemes.py @@ -1,69 +1,64 @@ -import requests import json -from pathlib import Path from typing import Tuple, Optional +from pathlib import Path + +import requests # Get the directory this script is in SCRIPT_DIR = Path(__file__).parent.absolute() + def get_phonemes(text: str, language: str = "a") -> Tuple[str, list[int]]: """Get phonemes and tokens for input text. - + Args: text: Input text to convert to phonemes language: Language code (defaults to "a" for American English) - + Returns: Tuple of (phonemes string, token list) """ # Create the request payload - payload = { - "text": text, - "language": language - } - + payload = {"text": text, "language": language} + # Make POST request to the phonemize endpoint - response = requests.post( - "http://localhost:8880/text/phonemize", - json=payload - ) - + response = requests.post("http://localhost:8880/text/phonemize", json=payload) + # Raise exception for error status codes response.raise_for_status() - + # Parse the response result = response.json() return result["phonemes"], result["tokens"] -def generate_audio_from_phonemes(phonemes: str, voice: str = "af_bella", speed: float = 1.0) -> Optional[bytes]: + +def generate_audio_from_phonemes( + phonemes: str, voice: str = "af_bella", speed: float = 1.0 +) -> Optional[bytes]: """Generate audio from phonemes. - + Args: phonemes: Phoneme string to synthesize voice: Voice ID to use (defaults to af_bella) speed: Speed factor (defaults to 1.0) - + Returns: WAV audio bytes if successful, None if failed """ # Create the request payload - payload = { - "phonemes": phonemes, - "voice": voice, - "speed": speed - } - + payload = {"phonemes": phonemes, "voice": voice, "speed": speed} + # Make POST request to generate audio response = requests.post( - "http://localhost:8880/text/generate_from_phonemes", - json=payload + "http://localhost:8880/text/generate_from_phonemes", json=payload ) - + # Raise exception for error status codes response.raise_for_status() - + return response.content + def main(): # Example texts to convert examples = [ @@ -71,15 +66,15 @@ def main(): "How are you today? I am doing reasonably well, thank you for asking", """This is a test of the phoneme generation system. Do not be alarmed. This is only a test. If this were a real phoneme emergency, ' - you would be instructed to a phoneme shelter in your area.""" + you would be instructed to a phoneme shelter in your area.""", ] - + print("Generating phonemes and audio for example texts...\n") - + # Create output directory in same directory as script output_dir = SCRIPT_DIR / "output" output_dir.mkdir(exist_ok=True) - + for i, text in enumerate(examples): print(f"{len(text)}: Input text: {text}") try: @@ -87,22 +82,23 @@ def main(): phonemes, tokens = get_phonemes(text) print(f"{len(phonemes)} Phonemes: {phonemes}") print(f"{len(tokens)} Tokens: {tokens}") - + # Generate audio from phonemes print("Generating audio...") audio_bytes = generate_audio_from_phonemes(phonemes) - + if audio_bytes: # Save audio file output_path = output_dir / f"example_{i+1}.wav" with output_path.open("wb") as f: f.write(audio_bytes) print(f"Audio saved to: {output_path}") - + print() - + except requests.RequestException as e: print(f"Error: {e}\n") + if __name__ == "__main__": main() diff --git a/examples/stream_tts_playback.py b/examples/stream_tts_playback.py index 70999a8..b72a8ee 100644 --- a/examples/stream_tts_playback.py +++ b/examples/stream_tts_playback.py @@ -1,17 +1,19 @@ #!/usr/bin/env python3 -import requests -import numpy as np -import sounddevice as sd -import time import os +import time import wave +import numpy as np +import requests +import sounddevice as sd + + def play_streaming_tts(text: str, output_file: str = None, voice: str = "af"): """Stream TTS audio and play it back in real-time""" - + print("\nStarting TTS stream request...") start_time = time.time() - + # Initialize variables sample_rate = 24000 # Known sample rate for Kokoro audio_started = False @@ -19,17 +21,17 @@ def play_streaming_tts(text: str, output_file: str = None, voice: str = "af"): total_bytes = 0 first_chunk_time = None all_audio_data = bytearray() # Raw PCM audio data - + # Start sounddevice stream with buffer stream = sd.OutputStream( samplerate=sample_rate, channels=1, dtype=np.int16, blocksize=1024, # Buffer size in samples - latency='low' # Request low latency + latency="low", # Request low latency ) stream.start() - + # Make streaming request to API try: response = requests.post( @@ -39,39 +41,45 @@ def play_streaming_tts(text: str, output_file: str = None, voice: str = "af"): "input": text, "voice": voice, "response_format": "pcm", - "stream": True + "stream": True, }, stream=True, - timeout=1800 + timeout=1800, ) response.raise_for_status() print(f"Request started successfully after {time.time() - start_time:.2f}s") - + # Process streaming response with smaller chunks for lower latency - for chunk in response.iter_content(chunk_size=512): # 512 bytes = 256 samples at 16-bit + for chunk in response.iter_content( + chunk_size=512 + ): # 512 bytes = 256 samples at 16-bit if chunk: chunk_count += 1 total_bytes += len(chunk) - + # Handle first chunk if not audio_started: first_chunk_time = time.time() - print(f"\nReceived first chunk after {first_chunk_time - start_time:.2f}s") + print( + f"\nReceived first chunk after {first_chunk_time - start_time:.2f}s" + ) print(f"First chunk size: {len(chunk)} bytes") audio_started = True - + # Convert bytes to numpy array and play audio_chunk = np.frombuffer(chunk, dtype=np.int16) stream.write(audio_chunk) - + # Accumulate raw audio data all_audio_data.extend(chunk) - + # Log progress every 10 chunks if chunk_count % 10 == 0: elapsed = time.time() - start_time - print(f"Progress: {chunk_count} chunks, {total_bytes/1024:.1f}KB received, {elapsed:.1f}s elapsed") - + print( + f"Progress: {chunk_count} chunks, {total_bytes/1024:.1f}KB received, {elapsed:.1f}s elapsed" + ) + # Final stats total_time = time.time() - start_time print(f"\nStream complete:") @@ -79,21 +87,21 @@ def play_streaming_tts(text: str, output_file: str = None, voice: str = "af"): print(f"Total data: {total_bytes/1024:.1f}KB") print(f"Total time: {total_time:.2f}s") print(f"Average speed: {(total_bytes/1024)/total_time:.1f}KB/s") - + # Save as WAV file if output_file: print(f"\nWriting audio to {output_file}") - with wave.open(output_file, 'wb') as wav_file: + with wave.open(output_file, "wb") as wav_file: wav_file.setnchannels(1) # Mono wav_file.setsampwidth(2) # 2 bytes per sample (16-bit) wav_file.setframerate(sample_rate) wav_file.writeframes(all_audio_data) print(f"Saved {len(all_audio_data)} bytes of audio data") - + # Clean up stream.stop() stream.close() - + except requests.exceptions.ConnectionError as e: print(f"Connection error - Is the server running? Error: {str(e)}") stream.stop() @@ -103,23 +111,27 @@ def play_streaming_tts(text: str, output_file: str = None, voice: str = "af"): stream.stop() stream.close() + def main(): # Load sample text from HG Wells script_dir = os.path.dirname(os.path.abspath(__file__)) - wells_path = os.path.join(script_dir, "assorted_checks/benchmarks/the_time_machine_hg_wells.txt") + wells_path = os.path.join( + script_dir, "assorted_checks/benchmarks/the_time_machine_hg_wells.txt" + ) output_path = os.path.join(script_dir, "output.wav") - + with open(wells_path, "r", encoding="utf-8") as f: full_text = f.read() # Take first few paragraphs text = " ".join(full_text.split("\n\n")[:2]) - + print("\nStarting TTS stream playback...") print(f"Text length: {len(text)} characters") print("\nFirst 100 characters:") print(text[:100] + "...") - + play_streaming_tts(text, output_file=output_path) + if __name__ == "__main__": main()