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whisper_online_server.py
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whisper_online_server.py
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#!/usr/bin/env python3
from whisper_online import *
import sys
import argparse
import os
import numpy as np
parser = argparse.ArgumentParser()
# server options
parser.add_argument("--host", type=str, default='localhost')
parser.add_argument("--port", type=int, default=43007)
# options from whisper_online
add_shared_args(parser)
args = parser.parse_args()
# setting whisper object by args
SAMPLING_RATE = 16000
size = args.model
language = args.lan
t = time.time()
print(f"Loading Whisper {size} model for {language}...",file=sys.stderr,end=" ",flush=True)
if args.backend == "faster-whisper":
from faster_whisper import WhisperModel
asr_cls = FasterWhisperASR
elif args.backend == "openai-api":
asr_cls = OpenaiApiASR
else:
import whisper
import whisper_timestamped
# from whisper_timestamped_model import WhisperTimestampedASR
asr_cls = WhisperTimestampedASR
asr = asr_cls(modelsize=size, lan=language, cache_dir=args.model_cache_dir, model_dir=args.model_dir)
if args.task == "translate":
asr.set_translate_task()
tgt_language = "en"
else:
tgt_language = language
e = time.time()
print(f"done. It took {round(e-t,2)} seconds.",file=sys.stderr)
if args.vad:
print("setting VAD filter",file=sys.stderr)
asr.use_vad()
min_chunk = args.min_chunk_size
if args.buffer_trimming == "sentence":
tokenizer = create_tokenizer(tgt_language)
else:
tokenizer = None
online = OnlineASRProcessor(asr,tokenizer,buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec))
demo_audio_path = "cs-maji-2.16k.wav"
if os.path.exists(demo_audio_path):
# load the audio into the LRU cache before we start the timer
a = load_audio_chunk(demo_audio_path,0,1)
# TODO: it should be tested whether it's meaningful
# warm up the ASR, because the very first transcribe takes much more time than the other
asr.transcribe(a)
else:
print("Whisper is not warmed up",file=sys.stderr)
######### Server objects
import line_packet
import socket
import logging
class Connection:
'''it wraps conn object'''
PACKET_SIZE = 65536
def __init__(self, conn):
self.conn = conn
self.last_line = ""
self.conn.setblocking(True)
def send(self, line):
'''it doesn't send the same line twice, because it was problematic in online-text-flow-events'''
if line == self.last_line:
return
line_packet.send_one_line(self.conn, line)
self.last_line = line
def receive_lines(self):
in_line = line_packet.receive_lines(self.conn)
return in_line
def non_blocking_receive_audio(self):
r = self.conn.recv(self.PACKET_SIZE)
return r
import io
import soundfile
# wraps socket and ASR object, and serves one client connection.
# next client should be served by a new instance of this object
class ServerProcessor:
def __init__(self, c, online_asr_proc, min_chunk):
self.connection = c
self.online_asr_proc = online_asr_proc
self.min_chunk = min_chunk
self.last_end = None
def receive_audio_chunk(self):
# receive all audio that is available by this time
# blocks operation if less than self.min_chunk seconds is available
# unblocks if connection is closed or a chunk is available
out = []
while sum(len(x) for x in out) < self.min_chunk*SAMPLING_RATE:
raw_bytes = self.connection.non_blocking_receive_audio()
print(raw_bytes[:10])
print(len(raw_bytes))
if not raw_bytes:
break
sf = soundfile.SoundFile(io.BytesIO(raw_bytes), channels=1,endian="LITTLE",samplerate=SAMPLING_RATE, subtype="PCM_16",format="RAW")
audio, _ = librosa.load(sf,sr=SAMPLING_RATE,dtype=np.float32)
out.append(audio)
if not out:
return None
return np.concatenate(out)
def format_output_transcript(self,o):
# output format in stdout is like:
# 0 1720 Takhle to je
# - the first two words are:
# - beg and end timestamp of the text segment, as estimated by Whisper model. The timestamps are not accurate, but they're useful anyway
# - the next words: segment transcript
# This function differs from whisper_online.output_transcript in the following:
# succeeding [beg,end] intervals are not overlapping because ELITR protocol (implemented in online-text-flow events) requires it.
# Therefore, beg, is max of previous end and current beg outputed by Whisper.
# Usually it differs negligibly, by appx 20 ms.
if o[0] is not None:
beg, end = o[0]*1000,o[1]*1000
if self.last_end is not None:
beg = max(beg, self.last_end)
self.last_end = end
print("%1.0f %1.0f %s" % (beg,end,o[2]),flush=True,file=sys.stderr)
return "%1.0f %1.0f %s" % (beg,end,o[2])
else:
print(o,file=sys.stderr,flush=True)
return None
def send_result(self, o):
msg = self.format_output_transcript(o)
if msg is not None:
self.connection.send(msg)
def process(self):
# handle one client connection
self.online_asr_proc.init()
while True:
a = self.receive_audio_chunk()
if a is None:
print("break here",file=sys.stderr)
break
self.online_asr_proc.insert_audio_chunk(a)
o = online.process_iter()
try:
self.send_result(o)
except BrokenPipeError:
print("broken pipe -- connection closed?",file=sys.stderr)
break
# o = online.finish() # this should be working
# self.send_result(o)
# Start logging.
level = logging.INFO
logging.basicConfig(level=level, format='whisper-server-%(levelname)s: %(message)s')
# server loop
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((args.host, args.port))
s.listen(1)
logging.info('INFO: Listening on'+str((args.host, args.port)))
while True:
conn, addr = s.accept()
logging.info('INFO: Connected to client on {}'.format(addr))
connection = Connection(conn)
proc = ServerProcessor(connection, online, min_chunk)
proc.process()
conn.close()
logging.info('INFO: Connection to client closed')
logging.info('INFO: Connection closed, terminating.')