mirror of
https://github.com/QuentinFuxa/WhisperLiveKit.git
synced 2026-03-07 22:33:36 +00:00
141 lines
5.0 KiB
Python
141 lines
5.0 KiB
Python
import io
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import argparse
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import asyncio
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import numpy as np
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import ffmpeg
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from time import time
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from fastapi import FastAPI, WebSocket, WebSocketDisconnect
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from fastapi.responses import HTMLResponse
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from fastapi.middleware.cors import CORSMiddleware
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from whisper_online import asr_factory, add_shared_args
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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parser = argparse.ArgumentParser(description="Whisper FastAPI Online Server")
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parser.add_argument("--host", type=str, default='localhost', help="The host address to bind the server to.")
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parser.add_argument("--port", type=int, default=8000, help="The port number to bind the server to.")
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parser.add_argument("--warmup-file", type=str, dest="warmup_file",
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help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .")
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add_shared_args(parser)
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args = parser.parse_args()
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asr, online = asr_factory(args)
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# Load demo HTML for the root endpoint
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with open("src/live_transcription.html", "r") as f:
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html = f.read()
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@app.get("/")
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async def get():
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return HTMLResponse(html)
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SAMPLE_RATE = 16000
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CHANNELS = 1
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SAMPLES_PER_SEC = SAMPLE_RATE * int(args.min_chunk_size)
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BYTES_PER_SAMPLE = 2 # s16le = 2 bytes per sample
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BYTES_PER_SEC = SAMPLES_PER_SEC * BYTES_PER_SAMPLE
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async def start_ffmpeg_decoder():
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"""
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Start an FFmpeg process in async streaming mode that reads WebM from stdin
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and outputs raw s16le PCM on stdout. Returns the process object.
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"""
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process = (
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ffmpeg
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.input('pipe:0', format='webm')
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.output('pipe:1', format='s16le', acodec='pcm_s16le', ac=CHANNELS, ar=str(SAMPLE_RATE))
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.run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True)
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)
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return process
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@app.websocket("/asr")
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async def websocket_endpoint(websocket: WebSocket):
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await websocket.accept()
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print("WebSocket connection opened.")
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ffmpeg_process = await start_ffmpeg_decoder()
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pcm_buffer = bytearray()
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# Continuously read decoded PCM from ffmpeg stdout in a background task
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async def ffmpeg_stdout_reader():
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nonlocal pcm_buffer
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loop = asyncio.get_event_loop()
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full_transcription = ""
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beg = time()
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while True:
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try:
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elapsed_time = int(time() - beg)
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beg = time()
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chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 32000*elapsed_time)
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if not chunk: # The first chunk will be almost empty, FFmpeg is still starting up
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chunk = await loop.run_in_executor(None, ffmpeg_process.stdout.read, 4096)
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if not chunk: # FFmpeg might have closed
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print("FFmpeg stdout closed.")
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break
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pcm_buffer.extend(chunk)
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if len(pcm_buffer) >= BYTES_PER_SEC:
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# Convert int16 -> float32
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pcm_array = np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0
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pcm_buffer = bytearray()
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online.insert_audio_chunk(pcm_array)
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transcription = online.process_iter()[2]
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if args.vac:
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buffer = online.online.to_flush(online.online.transcript_buffer.buffer)[2] # We need to access the underlying online object to get the buffer
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else:
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buffer = online.to_flush(online.transcript_buffer.buffer)[2]
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if buffer in full_transcription: # With VAC, the buffer is not updated until the next chunk is processed
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buffer = ""
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await websocket.send_json({
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"transcription": transcription,
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"buffer": buffer
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})
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except Exception as e:
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print(f"Exception in ffmpeg_stdout_reader: {e}")
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break
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print("Exiting ffmpeg_stdout_reader...")
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stdout_reader_task = asyncio.create_task(ffmpeg_stdout_reader())
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try:
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while True:
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# Receive incoming WebM audio chunks from the client
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message = await websocket.receive_bytes()
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# Pass them to ffmpeg via stdin
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ffmpeg_process.stdin.write(message)
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ffmpeg_process.stdin.flush()
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except WebSocketDisconnect:
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print("WebSocket connection closed.")
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except Exception as e:
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print(f"Error in websocket loop: {e}")
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finally:
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# Clean up ffmpeg and the reader task
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try:
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ffmpeg_process.stdin.close()
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except:
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pass
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stdout_reader_task.cancel()
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try:
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ffmpeg_process.stdout.close()
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except:
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pass
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ffmpeg_process.wait()
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run("whisper_fastapi_online_server:app", host=args.host, port=args.port, reload=True) |