#!/usr/bin/env python3 from whisper_online import * import sys import argparse import os 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) 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.')