mirror of
https://github.com/QuentinFuxa/WhisperLiveKit.git
synced 2026-03-07 22:33:36 +00:00
Further tidying of print output, so by default there's little on the console
This commit is contained in:
Alex Young
@@ -4,6 +4,7 @@ import numpy as np
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import librosa
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from functools import lru_cache
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import time
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import logging
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@@ -57,7 +58,7 @@ 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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print("ignoring model_dir, not implemented",file=self.logfile)
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logging.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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@@ -97,7 +98,7 @@ class FasterWhisperASR(ASRBase):
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def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
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from faster_whisper import WhisperModel
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if model_dir is not None:
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print(f"Loading whisper model from model_dir {model_dir}. modelsize and cache_dir parameters are not used.",file=self.logfile)
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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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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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@@ -173,9 +174,11 @@ class HypothesisBuffer:
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c = " ".join([self.commited_in_buffer[-j][2] for j in range(1,i+1)][::-1])
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tail = " ".join(self.new[j-1][2] for j in range(1,i+1))
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if c == tail:
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print("removing last",i,"words:",file=self.logfile)
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words = []
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for j in range(i):
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print("\t",self.new.pop(0),file=self.logfile)
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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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break
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def flush(self):
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@@ -267,9 +270,9 @@ class OnlineASRProcessor:
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"""
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prompt, non_prompt = self.prompt()
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print("PROMPT:", prompt, file=self.logfile)
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print("CONTEXT:", non_prompt, file=self.logfile)
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print(f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}",file=self.logfile)
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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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res = self.asr.transcribe(self.audio_buffer, init_prompt=prompt)
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# transform to [(beg,end,"word1"), ...]
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@@ -278,8 +281,10 @@ class OnlineASRProcessor:
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self.transcript_buffer.insert(tsw, self.buffer_time_offset)
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o = self.transcript_buffer.flush()
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self.commited.extend(o)
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print(">>>>COMPLETE NOW:",self.to_flush(o),file=self.logfile,flush=True)
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print("INCOMPLETE:",self.to_flush(self.transcript_buffer.complete()),file=self.logfile,flush=True)
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completed = self.to_flush(o)
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logging.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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# there is a newly confirmed text
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@@ -303,18 +308,18 @@ 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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print(f"chunking segment",file=self.logfile)
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logging.debug(f"chunking segment")
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#self.chunk_at(t)
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print(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}",file=self.logfile)
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logging.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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print(self.commited,file=self.logfile)
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logging.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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print("\t\tSENT:",s,file=self.logfile)
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logging.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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@@ -322,7 +327,7 @@ 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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print(f"--- sentence chunked at {chunk_at:2.2f}",file=self.logfile)
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logging.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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@@ -339,12 +344,12 @@ 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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print(f"--- segment chunked at {e:2.2f}",file=self.logfile)
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logging.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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print(f"--- last segment not within commited area",file=self.logfile)
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logging.debug(f"--- last segment not within commited area")
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else:
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print(f"--- not enough segments to chunk",file=self.logfile)
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logging.debug(f"--- not enough segments to chunk")
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@@ -391,7 +396,7 @@ 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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print("last, noncommited:",f,file=self.logfile)
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logging.debug("last, noncommited: {f}")
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return f
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@@ -431,7 +436,7 @@ 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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print(f"{lan} code is not supported by wtpsplit. Going to use None lang_code option.", file=sys.stderr)
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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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lan = None
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from wtpsplit import WtP
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@@ -476,20 +481,20 @@ 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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print("No or one option from --offline and --comp_unaware are available, not both. Exiting.",file=logfile)
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logging.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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print("Audio duration is: %2.2f seconds" % duration, file=logfile)
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logging.info("Audio duration is: %2.2f seconds" % duration)
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size = args.model
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language = args.lan
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t = time.time()
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print(f"Loading Whisper {size} model for {language}...",file=logfile,end=" ",flush=True)
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logging.info(f"Loading Whisper {size} model for {language}...")
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if args.backend == "faster-whisper":
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asr_cls = FasterWhisperASR
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@@ -506,10 +511,10 @@ if __name__ == "__main__":
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e = time.time()
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print(f"done. It took {round(e-t,2)} seconds.",file=logfile)
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logging.info(f"done. It took {round(e-t,2)} seconds.")
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if args.vad:
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print("setting VAD filter",file=logfile)
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logging.info("setting VAD filter")
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asr.use_vad()
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@@ -543,16 +548,15 @@ if __name__ == "__main__":
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print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),file=logfile,flush=True)
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print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),flush=True)
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else:
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print(o,file=logfile,flush=True)
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print("here?", o,file=logfile,flush=True)
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if args.offline: ## offline mode processing (for testing/debugging)
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a = load_audio(audio_path)
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online.insert_audio_chunk(a)
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try:
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o = online.process_iter()
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except AssertionError:
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print("assertion error",file=logfile)
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pass
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except AssertionError as e:
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log.error(f"assertion error: {repr(e)}")
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else:
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output_transcript(o)
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now = None
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@@ -563,13 +567,13 @@ if __name__ == "__main__":
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online.insert_audio_chunk(a)
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try:
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o = online.process_iter()
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except AssertionError:
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print("assertion error",file=logfile)
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except AssertionError as e:
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logging.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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print(f"## last processed {end:.2f}s",file=logfile,flush=True)
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logging.debug(f"## last processed {end:.2f}s")
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if end >= duration:
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break
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@@ -595,13 +599,13 @@ if __name__ == "__main__":
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try:
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o = online.process_iter()
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except AssertionError:
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print("assertion error",file=logfile)
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except AssertionError as e:
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logging.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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print(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}",file=logfile,flush=True)
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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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if end >= duration:
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break
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@@ -39,6 +39,7 @@ logging.debug(f"Loading Whisper {size} model for {language}...")
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if args.backend == "faster-whisper":
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from faster_whisper import WhisperModel
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asr_cls = FasterWhisperASR
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logging.getLogger("faster_whisper").setLevel(logging.WARNING)
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else:
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import whisper
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import whisper_timestamped
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@@ -80,7 +81,7 @@ if os.path.exists(demo_audio_path):
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# warm up the ASR, because the very first transcribe takes much more time than the other
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asr.transcribe(a)
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else:
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logging.info("Whisper is not warmed up")
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logging.debug("Whisper is not warmed up")
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######### Server objects
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@@ -135,8 +136,6 @@ class ServerProcessor:
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out = []
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while sum(len(x) for x in out) < self.min_chunk*SAMPLING_RATE:
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raw_bytes = self.connection.non_blocking_receive_audio()
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print(raw_bytes[:10])
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print(len(raw_bytes))
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if not raw_bytes:
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break
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sf = soundfile.SoundFile(io.BytesIO(raw_bytes), channels=1,endian="LITTLE",samplerate=SAMPLING_RATE, subtype="PCM_16",format="RAW")
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@@ -167,7 +166,7 @@ class ServerProcessor:
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print("%1.0f %1.0f %s" % (beg,end,o[2]),flush=True,file=sys.stderr)
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return "%1.0f %1.0f %s" % (beg,end,o[2])
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else:
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print(o,file=sys.stderr,flush=True)
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# No text, so no output
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return None
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def send_result(self, o):
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@@ -181,14 +180,13 @@ class ServerProcessor:
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while True:
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a = self.receive_audio_chunk()
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if a is None:
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print("break here",file=sys.stderr)
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break
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self.online_asr_proc.insert_audio_chunk(a)
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o = online.process_iter()
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try:
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self.send_result(o)
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except BrokenPipeError:
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print("broken pipe -- connection closed?",file=sys.stderr)
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logging.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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