mirror of
https://github.com/QuentinFuxa/WhisperLiveKit.git
synced 2026-03-07 14:23:18 +00:00
Construct an explicit logger rather than using the root logger
This commit is contained in:
Alex Young
@@ -11,6 +11,8 @@ import io
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import soundfile as sf
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import math
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logger = logging.getLogger(__name__)
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@lru_cache
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def load_audio(fname):
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a, _ = librosa.load(fname, sr=16000, dtype=np.float32)
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@@ -65,7 +67,7 @@ class WhisperTimestampedASR(ASRBase):
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from whisper_timestamped import transcribe_timestamped
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self.transcribe_timestamped = transcribe_timestamped
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if model_dir is not None:
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logging.debug("ignoring model_dir, not implemented")
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logger.debug("ignoring model_dir, not implemented")
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return whisper.load_model(modelsize, download_root=cache_dir)
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def transcribe(self, audio, init_prompt=""):
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@@ -106,7 +108,7 @@ class FasterWhisperASR(ASRBase):
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from faster_whisper import WhisperModel
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logging.getLogger("faster_whisper").setLevel(logging.WARNING)
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if model_dir is not None:
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logging.debug(f"Loading whisper model from model_dir {model_dir}. modelsize and cache_dir parameters are not used.")
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logger.debug(f"Loading whisper model from model_dir {model_dir}. modelsize and cache_dir parameters are not used.")
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model_size_or_path = model_dir
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elif modelsize is not None:
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model_size_or_path = modelsize
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@@ -229,7 +231,7 @@ class OpenaiApiASR(ASRBase):
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# Process transcription/translation
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transcript = proc.create(**params)
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logging.debug(f"OpenAI API processed accumulated {self.transcribed_seconds} seconds")
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logger.debug(f"OpenAI API processed accumulated {self.transcribed_seconds} seconds")
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return transcript
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@@ -276,7 +278,7 @@ class HypothesisBuffer:
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for j in range(i):
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words.append(repr(self.new.pop(0)))
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words_msg = "\t".join(words)
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logging.debug(f"removing last {i} words: {words_msg}")
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logger.debug(f"removing last {i} words: {words_msg}")
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break
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def flush(self):
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@@ -365,9 +367,9 @@ class OnlineASRProcessor:
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"""
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prompt, non_prompt = self.prompt()
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logging.debug(f"PROMPT: {prompt}")
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logging.debug(f"CONTEXT: {non_prompt}")
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logging.debug(f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}")
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logger.debug(f"PROMPT: {prompt}")
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logger.debug(f"CONTEXT: {non_prompt}")
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logger.debug(f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}")
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res = self.asr.transcribe(self.audio_buffer, init_prompt=prompt)
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# transform to [(beg,end,"word1"), ...]
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@@ -377,9 +379,9 @@ class OnlineASRProcessor:
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o = self.transcript_buffer.flush()
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self.commited.extend(o)
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completed = self.to_flush(o)
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logging.debug(f">>>>COMPLETE NOW: {completed}")
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logger.debug(f">>>>COMPLETE NOW: {completed}")
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the_rest = self.to_flush(self.transcript_buffer.complete())
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logging.debug(f"INCOMPLETE: {the_rest}")
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logger.debug(f"INCOMPLETE: {the_rest}")
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# there is a newly confirmed text
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@@ -403,18 +405,18 @@ class OnlineASRProcessor:
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#while k>0 and self.commited[k][1] > l:
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# k -= 1
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#t = self.commited[k][1]
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logging.debug(f"chunking segment")
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logger.debug(f"chunking segment")
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#self.chunk_at(t)
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logging.debug(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}")
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logger.debug(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}")
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return self.to_flush(o)
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def chunk_completed_sentence(self):
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if self.commited == []: return
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logging.debug(self.commited)
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logger.debug(self.commited)
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sents = self.words_to_sentences(self.commited)
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for s in sents:
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logging.debug(f"\t\tSENT: {s}")
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logger.debug(f"\t\tSENT: {s}")
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if len(sents) < 2:
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return
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while len(sents) > 2:
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@@ -422,7 +424,7 @@ class OnlineASRProcessor:
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# we will continue with audio processing at this timestamp
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chunk_at = sents[-2][1]
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logging.debug(f"--- sentence chunked at {chunk_at:2.2f}")
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logger.debug(f"--- sentence chunked at {chunk_at:2.2f}")
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self.chunk_at(chunk_at)
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def chunk_completed_segment(self, res):
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@@ -439,12 +441,12 @@ class OnlineASRProcessor:
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ends.pop(-1)
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e = ends[-2]+self.buffer_time_offset
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if e <= t:
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logging.debug(f"--- segment chunked at {e:2.2f}")
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logger.debug(f"--- segment chunked at {e:2.2f}")
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self.chunk_at(e)
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else:
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logging.debug(f"--- last segment not within commited area")
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logger.debug(f"--- last segment not within commited area")
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else:
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logging.debug(f"--- not enough segments to chunk")
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logger.debug(f"--- not enough segments to chunk")
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@@ -490,7 +492,7 @@ class OnlineASRProcessor:
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"""
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o = self.transcript_buffer.complete()
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f = self.to_flush(o)
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logging.debug("last, noncommited: {f}")
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logger.debug("last, noncommited: {f}")
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return f
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@@ -530,7 +532,7 @@ def create_tokenizer(lan):
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# the following languages are in Whisper, but not in wtpsplit:
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if lan in "as ba bo br bs fo haw hr ht jw lb ln lo mi nn oc sa sd sn so su sw tk tl tt".split():
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logging.debug(f"{lan} code is not supported by wtpsplit. Going to use None lang_code option.")
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logger.debug(f"{lan} code is not supported by wtpsplit. Going to use None lang_code option.")
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lan = None
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from wtpsplit import WtP
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@@ -563,7 +565,7 @@ def asr_factory(args, logfile=sys.stderr):
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"""
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backend = args.backend
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if backend == "openai-api":
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logging.debug("Using OpenAI API.")
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logger.debug("Using OpenAI API.")
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asr = OpenaiApiASR(lan=args.lan)
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else:
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if backend == "faster-whisper":
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@@ -574,14 +576,14 @@ def asr_factory(args, logfile=sys.stderr):
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# Only for FasterWhisperASR and WhisperTimestampedASR
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size = args.model
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t = time.time()
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logging.debug(f"Loading Whisper {size} model for {args.lan}...")
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logger.debug(f"Loading Whisper {size} model for {args.lan}...")
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asr = asr_cls(modelsize=size, lan=args.lan, cache_dir=args.model_cache_dir, model_dir=args.model_dir)
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e = time.time()
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logging.debug(f"done. It took {round(e-t,2)} seconds.")
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logger.debug(f"done. It took {round(e-t,2)} seconds.")
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# Apply common configurations
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if getattr(args, 'vad', False): # Checks if VAD argument is present and True
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logging.info("Setting VAD filter")
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logger.info("Setting VAD filter")
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asr.use_vad()
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language = args.lan
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@@ -619,14 +621,14 @@ if __name__ == "__main__":
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logfile = sys.stderr
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if args.offline and args.comp_unaware:
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logging.error("No or one option from --offline and --comp_unaware are available, not both. Exiting.")
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logger.error("No or one option from --offline and --comp_unaware are available, not both. Exiting.")
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sys.exit(1)
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audio_path = args.audio_path
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SAMPLING_RATE = 16000
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duration = len(load_audio(audio_path))/SAMPLING_RATE
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logging.info("Audio duration is: %2.2f seconds" % duration)
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logger.info("Audio duration is: %2.2f seconds" % duration)
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asr, online = asr_factory(args, logfile=logfile)
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min_chunk = args.min_chunk_size
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@@ -674,12 +676,12 @@ if __name__ == "__main__":
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try:
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o = online.process_iter()
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except AssertionError as e:
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logging.error(f"assertion error: {repr(e)}")
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logger.error(f"assertion error: {repr(e)}")
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pass
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else:
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output_transcript(o, now=end)
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logging.debug(f"## last processed {end:.2f}s")
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logger.debug(f"## last processed {end:.2f}s")
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if end >= duration:
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break
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@@ -706,12 +708,12 @@ if __name__ == "__main__":
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try:
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o = online.process_iter()
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except AssertionError as e:
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logging.error(f"assertion error: {e}")
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logger.error(f"assertion error: {e}")
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pass
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else:
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output_transcript(o)
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now = time.time() - start
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logging.debug(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}")
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logger.debug(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}")
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if end >= duration:
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break
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@@ -7,6 +7,8 @@ import os
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import logging
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import numpy as np
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logger = logging.getLogger(__name__)
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print(__name__)
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parser = argparse.ArgumentParser()
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# server options
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@@ -38,13 +40,6 @@ language = args.lan
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asr, online = asr_factory(args)
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min_chunk = args.min_chunk_size
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if args.buffer_trimming == "sentence":
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tokenizer = create_tokenizer(tgt_language)
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else:
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tokenizer = None
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online = OnlineASRProcessor(asr,tokenizer,buffer_trimming=(args.buffer_trimming, args.buffer_trimming_sec))
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# warm up the ASR because the very first transcribe takes more time than the others.
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# Test results in https://github.com/ufal/whisper_streaming/pull/81
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msg = "Whisper is not warmed up. The first chunk processing may take longer."
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@@ -161,7 +156,7 @@ class ServerProcessor:
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try:
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self.send_result(o)
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except BrokenPipeError:
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logging.info("broken pipe -- connection closed?")
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logger.info("broken pipe -- connection closed?")
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break
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# o = online.finish() # this should be working
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@@ -175,13 +170,13 @@ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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s.bind((args.host, args.port))
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s.listen(1)
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logging.info('Listening on'+str((args.host, args.port)))
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logger.info('Listening on'+str((args.host, args.port)))
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while True:
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conn, addr = s.accept()
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logging.info('Connected to client on {}'.format(addr))
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logger.info('Connected to client on {}'.format(addr))
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connection = Connection(conn)
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proc = ServerProcessor(connection, online, min_chunk)
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proc.process()
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conn.close()
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logging.info('Connection to client closed')
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logging.info('Connection closed, terminating.')
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logger.info('Connection to client closed')
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logger.info('Connection closed, terminating.')
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