mirror of
https://github.com/remsky/Kokoro-FastAPI.git
synced 2025-04-13 09:39:17 +00:00
More working on streaming timestamps
This commit is contained in:
Fireblade2534
parent
51b6b01589
commit
91d370d97f
2 changed files with 35 additions and 31 deletions
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@ -5,9 +5,10 @@ import json
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import os
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import re
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import tempfile
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from typing import AsyncGenerator, Dict, List, Union
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from typing import AsyncGenerator, Dict, List, Union, Tuple
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import aiofiles
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from inference.base import AudioChunk
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import torch
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from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response
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from fastapi.responses import FileResponse, StreamingResponse
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@ -127,13 +128,13 @@ async def process_voices(
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async def stream_audio_chunks(
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tts_service: TTSService, request: OpenAISpeechRequest, client_request: Request
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) -> AsyncGenerator[bytes, None]:
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) -> AsyncGenerator[Tuple[bytes,AudioChunk], None]:
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"""Stream audio chunks as they're generated with client disconnect handling"""
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voice_name = await process_voices(request.voice, tts_service)
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try:
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logger.info(f"Starting audio generation with lang_code: {request.lang_code}")
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async for chunk in tts_service.generate_audio_stream(
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async for chunk, chunk_data in tts_service.generate_audio_stream(
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text=request.input,
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voice=voice_name,
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speed=request.speed,
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@ -148,7 +149,7 @@ async def stream_audio_chunks(
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if is_disconnected:
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logger.info("Client disconnected, stopping audio generation")
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break
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yield chunk
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yield chunk, chunk_data
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except Exception as e:
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logger.error(f"Error in audio streaming: {str(e)}")
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# Let the exception propagate to trigger cleanup
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@ -213,12 +214,15 @@ async def create_speech(
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}
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# Create async generator for streaming
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async def dual_output():
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async def dual_output(return_json:bool=False):
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try:
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# Write chunks to temp file and stream
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async for chunk in generator:
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async for chunk, chunk_data in generator:
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if chunk: # Skip empty chunks
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await temp_writer.write(chunk)
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if return_json:
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yield chunk, chunk_data
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else:
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yield chunk
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# Finalize the temp file
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@ -53,7 +53,8 @@ class TTSService:
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is_last: bool = False,
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normalizer: Optional[AudioNormalizer] = None,
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lang_code: Optional[str] = None,
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) -> AsyncGenerator[Union[np.ndarray, bytes], None]:
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return_timestamps: Optional[bool] = False,
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) -> AsyncGenerator[Tuple[Union[np.ndarray, bytes],AudioChunk], Tuple[None,None]]:
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"""Process tokens into audio."""
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async with self._chunk_semaphore:
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try:
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@ -63,7 +64,7 @@ class TTSService:
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if not output_format:
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yield np.array([], dtype=np.float32)
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return
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result, _ = await AudioService.convert_audio(
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result, chunk_data = await AudioService.convert_audio(
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AudioChunk(np.array([0], dtype=np.float32)), # Dummy data for type checking
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24000,
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output_format,
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@ -73,7 +74,7 @@ class TTSService:
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normalizer=normalizer,
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is_last_chunk=True,
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)
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yield result
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yield result, chunk_data
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return
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# Skip empty chunks
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@ -91,7 +92,7 @@ class TTSService:
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(voice_name, voice_path),
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speed=speed,
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lang_code=lang_code,
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return_timestamps=True,
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return_timestamps=return_timestamps,
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):
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# For streaming, convert to bytes
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if output_format:
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@ -106,8 +107,7 @@ class TTSService:
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is_last_chunk=is_last,
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normalizer=normalizer,
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)
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print(chunk_data.word_timestamps)
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yield converted
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yield converted, chunk_data
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except Exception as e:
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logger.error(f"Failed to convert audio: {str(e)}")
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else:
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@ -116,31 +116,30 @@ class TTSService:
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speed,
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is_last,
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normalizer)
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print(chunk_data.word_timestamps)
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yield chunk_data.audio
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yield chunk_data.audio, chunk_data
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else:
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# For legacy backends, load voice tensor
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voice_tensor = await self._voice_manager.load_voice(
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voice_name, device=backend.device
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)
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chunk_audio = await self.model_manager.generate(
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tokens, voice_tensor, speed=speed
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chunk_data = await self.model_manager.generate(
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tokens, voice_tensor, speed=speed, return_timestamps=return_timestamps
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)
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if chunk_audio is None:
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if chunk_data.audio is None:
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logger.error("Model generated None for audio chunk")
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return
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if len(chunk_audio) == 0:
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if len(chunk_data.audio) == 0:
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logger.error("Model generated empty audio chunk")
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return
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# For streaming, convert to bytes
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if output_format:
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try:
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converted = await AudioService.convert_audio(
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chunk_audio,
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converted, chunk_data = await AudioService.convert_audio(
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chunk_data,
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24000,
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output_format,
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speed,
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@ -149,16 +148,16 @@ class TTSService:
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normalizer=normalizer,
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is_last_chunk=is_last,
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)
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yield converted
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yield converted, chunk_data
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except Exception as e:
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logger.error(f"Failed to convert audio: {str(e)}")
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else:
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trimmed = await AudioService.trim_audio(chunk_audio,
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trimmed = await AudioService.trim_audio(chunk_data,
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chunk_text,
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speed,
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is_last,
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normalizer)
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yield trimmed
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yield trimmed.audio, trimmed
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except Exception as e:
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logger.error(f"Failed to process tokens: {str(e)}")
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@ -242,8 +241,9 @@ class TTSService:
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speed: float = 1.0,
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output_format: str = "wav",
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lang_code: Optional[str] = None,
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normalization_options: Optional[NormalizationOptions] = NormalizationOptions()
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) -> AsyncGenerator[bytes, None]:
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normalization_options: Optional[NormalizationOptions] = NormalizationOptions(),
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return_timestamps: Optional[bool] = False,
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) -> AsyncGenerator[Tuple[bytes,AudioChunk], None]:
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"""Generate and stream audio chunks."""
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stream_normalizer = AudioNormalizer()
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chunk_index = 0
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@ -266,7 +266,7 @@ class TTSService:
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async for chunk_text, tokens in smart_split(text,normalization_options=normalization_options):
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try:
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# Process audio for chunk
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async for result in self._process_chunk(
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async for result, chunk_data in self._process_chunk(
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chunk_text, # Pass text for Kokoro V1
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tokens, # Pass tokens for legacy backends
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voice_name, # Pass voice name
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@ -279,7 +279,7 @@ class TTSService:
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lang_code=pipeline_lang_code, # Pass lang_code
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):
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if result is not None:
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yield result
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yield result,chunk_data
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chunk_index += 1
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else:
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logger.warning(
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@ -296,7 +296,7 @@ class TTSService:
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if chunk_index > 0:
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try:
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# Empty tokens list to finalize audio
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async for result in self._process_chunk(
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async for result,chunk_data in self._process_chunk(
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"", # Empty text
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[], # Empty tokens
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voice_name,
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@ -309,7 +309,7 @@ class TTSService:
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lang_code=pipeline_lang_code, # Pass lang_code
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):
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if result is not None:
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yield result
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yield result, chunk_data
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except Exception as e:
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logger.error(f"Failed to finalize audio stream: {str(e)}")
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@ -325,7 +325,7 @@ class TTSService:
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speed: float = 1.0,
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return_timestamps: bool = False,
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lang_code: Optional[str] = None,
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) -> Union[Tuple[np.ndarray, float], Tuple[np.ndarray, float, List[dict]]]:
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) -> Tuple[Tuple[np.ndarray,AudioChunk]]:
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"""Generate complete audio for text using streaming internally."""
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start_time = time.time()
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chunks = []
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