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Refactor TTS service to improve filename safety and audio chunk handling. Updated filename regex to allow additional characters, enhanced silence chunk creation for AudioService, and ensured final audio output is consistently in int16 format. Removed premature writer closure in the finalization process, delegating responsibility to the caller.

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This commit is contained in:
Lukin 2025-04-08 09:38:59 +08:00
parent 4b334beff4
commit 207d709de1
2 changed files with 61 additions and 22 deletions
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62
api/src/services/tts_service.py
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@ -270,7 +270,7 @@ class TTSService:
# Save the new combined voice so it can be loaded later
# Use a safe filename based on the original input string
safe_filename = re.sub(r'[^\w+-]', '_', voice) + ".pt"
safe_filename = re.sub(r'[^\w+-.\(\)]', '_', voice) + ".pt" # Allow weights in filename
temp_dir = tempfile.gettempdir()
combined_path = os.path.join(temp_dir, safe_filename)
logger.debug(f"Saving combined voice '{voice}' to temporary path: {combined_path}")
@ -328,6 +328,7 @@ class TTSService:
try:
logger.debug(f"Generating {pause_duration_s}s silence chunk")
silence_samples = int(pause_duration_s * settings.sample_rate)
# Create silence appropriate for AudioService (float32)
silence_audio = np.zeros(silence_samples, dtype=np.float32)
pause_chunk = AudioChunk(audio=silence_audio, word_timestamps=[]) # Empty timestamps for silence
@ -340,13 +341,14 @@ class TTSService:
if formatted_pause_chunk.output:
yield formatted_pause_chunk
else: # Raw audio mode
# Normalize to int16 for raw output consistency
pause_chunk.audio = stream_normalizer.normalize(pause_chunk.audio)
if len(pause_chunk.audio) > 0:
yield pause_chunk
# Update offset based on silence duration
current_offset += pause_duration_s
chunk_index += 1
chunk_index += 1 # Count pause as a yielded chunk
except Exception as e:
logger.error(f"Failed to process pause chunk: {str(e)}")
@ -368,8 +370,8 @@ class TTSService:
speed,
writer,
output_format,
is_first=(chunk_index == 0),
is_last=False,
is_first=(chunk_index == 0), # Check if this is the very first *audio* chunk
is_last=False, # is_last is handled separately after the loop
normalizer=stream_normalizer,
lang_code=pipeline_lang_code,
return_timestamps=return_timestamps,
@ -381,7 +383,6 @@ class TTSService:
timestamp.end_time += current_offset
# Update offset based on the *actual duration* of the generated audio chunk
# Check if audio data exists before calculating duration
chunk_duration = 0
if chunk_data.audio is not None and len(chunk_data.audio) > 0:
chunk_duration = len(chunk_data.audio) / settings.sample_rate
@ -397,7 +398,6 @@ class TTSService:
f"No audio generated or output for text chunk: '{text_chunk_for_model[:50]}...'"
)
# --- Add pause after newline (if applicable) ---
if has_trailing_newline:
newline_pause_s = 0.5
@ -445,20 +445,21 @@ class TTSService:
"", [], voice_name, voice_path, speed, writer, output_format,
is_first=False, is_last=True, normalizer=stream_normalizer, lang_code=pipeline_lang_code
):
# Yield final formatted chunk or raw empty chunk
if output_format and final_chunk_data.output:
yield final_chunk_data
elif not output_format and final_chunk_data.audio is not None and len(final_chunk_data.audio) > 0:
yield final_chunk_data # Should yield empty chunk in raw mode upon finalize
elif not output_format: # Raw mode: Finalize yields empty chunk signal
yield final_chunk_data # Yields empty AudioChunk
except Exception as e:
logger.error(f"Failed to finalize audio stream: {str(e)}")
except Exception as e:
logger.exception(f"Error during audio stream generation: {str(e)}") # Use exception for traceback
# Ensure writer is closed on error
try:
writer.close()
except Exception as close_e:
logger.error(f"Error closing writer during exception handling: {close_e}")
# Ensure writer is closed on error - moved to caller (e.g., route handler)
# try:
# writer.close()
# except Exception as close_e:
# logger.error(f"Error closing writer during exception handling: {close_e}")
raise e # Re-raise the original exception
@ -477,38 +478,51 @@ class TTSService:
output_format = None # Signal raw audio mode for internal streaming
combined_chunk = None
try:
# Pass a dummy writer if none provided, as generate_audio_stream requires one
# Although in raw mode (output_format=None), it shouldn't be heavily used for formatting
internal_writer = writer if writer else StreamingAudioWriter(format='wav', sample_rate=settings.sample_rate)
async for audio_stream_data in self.generate_audio_stream(
text,
voice,
writer, # Pass writer, although it won't be used for formatting here
internal_writer, # Pass the writer instance
speed=speed,
normalization_options=normalization_options,
return_timestamps=return_timestamps,
return_timestamps=return_timestamps, # Pass this down
lang_code=lang_code,
output_format=output_format, # Explicitly None for raw audio
):
# Ensure we only append chunks with actual audio data
# Raw silence chunks generated for pauses will have audio data (zeros)
if audio_stream_data.audio is not None and len(audio_stream_data.audio) > 0:
# Ensure timestamps are preserved if requested
if return_timestamps and not audio_stream_data.word_timestamps:
audio_stream_data.word_timestamps = [] # Initialize if needed
audio_data_chunks.append(audio_stream_data)
if not audio_data_chunks:
logger.warning("No valid audio chunks generated.")
combined_chunk = AudioChunk(audio=np.array([], dtype=np.int16), word_timestamps=[])
else:
combined_chunk = AudioChunk.combine(audio_data_chunks)
# Ensure the combined audio is int16 before returning, as downstream expects this raw format.
if combined_chunk.audio.dtype != np.int16:
logger.warning(f"Combined audio dtype is {combined_chunk.audio.dtype}, converting to int16.")
# Assuming normalization happened, scale from float [-1, 1] to int16
if np.issubdtype(combined_chunk.audio.dtype, np.floating):
combined_chunk.audio = np.clip(combined_chunk.audio * 32767, -32768, 32767).astype(np.int16)
else:
# If it's another type, attempt direct conversion (might be lossy)
combined_chunk.audio = combined_chunk.audio.astype(np.int16)
return combined_chunk
except Exception as e:
logger.error(f"Error in combined audio generation: {str(e)}")
raise # Re-raise after logging
finally:
# Explicitly close the writer if it was passed, though it shouldn't hold resources in raw mode
try:
writer.close()
except Exception:
pass # Ignore errors during cleanup
# Removed finally block that closed the writer prematurely
# The caller is now responsible for closing the writer after final conversion.
async def combine_voices(self, voices: List[str]) -> torch.Tensor:
@ -584,6 +598,10 @@ class TTSService:
# Normalize the final audio before returning
normalizer = AudioNormalizer()
normalized_audio = normalizer.normalize(result_audio)
# Return as int16 for consistency
if normalized_audio.dtype != np.int16:
normalized_audio = np.clip(normalized_audio * 32767, -32768, 32767).astype(np.int16)
return normalized_audio, processing_time
else:
raise ValueError(

21
run-tests.sh Executable file
View file

@ -0,0 +1,21 @@
#!/bin/bash
# Get project root directory
PROJECT_ROOT=$(pwd)
# Set environment variables
export USE_GPU=false
export USE_ONNX=false
export PYTHONPATH=$PROJECT_ROOT:$PROJECT_ROOT/api
export MODEL_DIR=src/models
export VOICES_DIR=src/voices/v1_0
export WEB_PLAYER_PATH=$PROJECT_ROOT/web
# Set the espeak-ng data path to your location
export ESPEAK_DATA_PATH=/usr/lib/x86_64-linux-gnu/espeak-ng-data
# Run FastAPI with CPU extras using uv run
# Note: espeak may still require manual installation,
uv pip install -e ".[test,cpu]"
uv run --no-sync python docker/scripts/download_model.py --output api/src/models/v1_0
uv run pytest api/tests/ --asyncio-mode=auto --cov=api --cov-report=term-missing