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import io
import argparse
import asyncio
import numpy as np
import ffmpeg
from time import time
from contextlib import asynccontextmanager
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from src.whisper_streaming.whisper_online import backend_factory, online_factory, add_shared_args
import subprocess
import math
##### LOAD ARGS #####
parser = argparse.ArgumentParser(description="Whisper FastAPI Online Server")
parser.add_argument(
"--host",
type=str,
default="localhost",
help="The host address to bind the server to.",
)
parser.add_argument(
"--port", type=int, default=8000, help="The port number to bind the server to."
)
parser.add_argument(
"--warmup-file",
type=str,
dest="warmup_file",
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 .",
)
parser.add_argument(
"--diarization",
type=bool,
default=False,
help="Whether to enable speaker diarization.",
)
add_shared_args(parser)
args = parser.parse_args()
SAMPLE_RATE = 16000
CHANNELS = 1
SAMPLES_PER_SEC = SAMPLE_RATE * int(args.min_chunk_size)
BYTES_PER_SAMPLE = 2 # s16le = 2 bytes per sample
BYTES_PER_SEC = SAMPLES_PER_SEC * BYTES_PER_SAMPLE
if args.diarization:
from src.diarization.diarization_online import DiartDiarization
##### LOAD APP #####
@asynccontextmanager
async def lifespan(app: FastAPI):
global asr, tokenizer
asr, tokenizer = backend_factory(args)
yield
app = FastAPI(lifespan=lifespan)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Load demo HTML for the root endpoint
with open("src/web/live_transcription.html", "r", encoding="utf-8") as f:
html = f.read()
async def start_ffmpeg_decoder():
"""
Start an FFmpeg process in async streaming mode that reads WebM from stdin
and outputs raw s16le PCM on stdout. Returns the process object.
"""
process = (
ffmpeg.input("pipe:0", format="webm")
.output(
"pipe:1",
format="s16le",
acodec="pcm_s16le",
ac=CHANNELS,
ar=str(SAMPLE_RATE),
)
.run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True)
)
return process
##### ENDPOINTS #####
@app.get("/")
async def get():
return HTMLResponse(html)
@app.websocket("/asr")
async def websocket_endpoint(websocket: WebSocket):
await websocket.accept()
print("WebSocket connection opened.")
ffmpeg_process = await start_ffmpeg_decoder()
pcm_buffer = bytearray()
print("Loading online.")
online = online_factory(args, asr, tokenizer)
print("Online loaded.")
if args.diarization:
diarization = DiartDiarization(SAMPLE_RATE)
# Continuously read decoded PCM from ffmpeg stdout in a background task
async def ffmpeg_stdout_reader():
nonlocal pcm_buffer
loop = asyncio.get_event_loop()
full_transcription = ""
beg = time()
chunk_history = [] # Will store dicts: {beg, end, text, speaker}
while True:
try:
elapsed_time = math.floor((time() - beg) * 10) / 10 # Round to 0.1 sec
ffmpeg_buffer_from_duration = max(int(32000 * elapsed_time), 4096)
beg = time()
chunk = await loop.run_in_executor(
None, ffmpeg_process.stdout.read, ffmpeg_buffer_from_duration
)
if not chunk:
print("FFmpeg stdout closed.")
break
pcm_buffer.extend(chunk)
if len(pcm_buffer) >= BYTES_PER_SEC:
# Convert int16 -> float32
pcm_array = (
np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32)
/ 32768.0
)
pcm_buffer = bytearray()
online.insert_audio_chunk(pcm_array)
transcription = online.process_iter()
if transcription:
chunk_history.append({
"beg": transcription.start,
"end": transcription.end,
"text": transcription.text,
"speaker": "0"
})
full_transcription += transcription.text
buffer = online.get_buffer()
if buffer in full_transcription: # With VAC, the buffer is not updated until the next chunk is processed
buffer = ""
lines = [
{
"speaker": "0",
"text": "",
}
]
if args.diarization:
await diarization.diarize(pcm_array)
diarization.assign_speakers_to_chunks(chunk_history)
for ch in chunk_history:
if args.diarization and ch["speaker"] and ch["speaker"][-1] != lines[-1]["speaker"]:
lines.append(
{
"speaker": ch["speaker"][-1],
"text": ch['text']
}
)
else:
lines[-1]["text"] += ch['text']
response = {"lines": lines, "buffer": buffer}
await websocket.send_json(response)
except Exception as e:
print(f"Exception in ffmpeg_stdout_reader: {e}")
break
print("Exiting ffmpeg_stdout_reader...")
stdout_reader_task = asyncio.create_task(ffmpeg_stdout_reader())
try:
while True:
# Receive incoming WebM audio chunks from the client
message = await websocket.receive_bytes()
# Pass them to ffmpeg via stdin
ffmpeg_process.stdin.write(message)
ffmpeg_process.stdin.flush()
except WebSocketDisconnect:
print("WebSocket connection closed.")
except Exception as e:
print(f"Error in websocket loop: {e}")
finally:
# Clean up ffmpeg and the reader task
try:
ffmpeg_process.stdin.close()
except:
pass
stdout_reader_task.cancel()
try:
ffmpeg_process.stdout.close()
except:
pass
ffmpeg_process.wait()
del online
if args.diarization:
# Stop Diart
diarization.close()
if __name__ == "__main__":
import uvicorn
uvicorn.run(
"whisper_fastapi_online_server:app", host=args.host, port=args.port, reload=True,
log_level="info"
)
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