#!/usr/bin/env python3 import sys import numpy as np import librosa from functools import lru_cache import time from mosestokenizer import MosesTokenizer @lru_cache def load_audio(fname): a, _ = librosa.load(fname, sr=16000) return a def load_audio_chunk(fname, beg, end): audio = load_audio(fname) beg_s = int(beg*16000) end_s = int(end*16000) return audio[beg_s:end_s] # Whisper backend class ASRBase: def __init__(self, modelsize, lan, cache_dir): self.original_language = lan self.model = self.load_model(modelsize, cache_dir) def load_model(self, modelsize, cache_dir): raise NotImplemented("mus be implemented in the child class") def transcribe(self, audio, init_prompt=""): raise NotImplemented("mus be implemented in the child class") ## requires imports: # import whisper # import whisper_timestamped class WhisperTimestampedASR(ASRBase): """Uses whisper_timestamped library as the backend. Initially, we tested the code on this backend. It worked, but slower than faster-whisper. On the other hand, the installation for GPU could be easier. If used, requires imports: import whisper import whisper_timestamped """ def load_model(self, modelsize, cache_dir): return whisper.load_model(modelsize, download_root=cache_dir) def transcribe(self, audio, init_prompt=""): result = whisper_timestamped.transcribe_timestamped(self.model, audio, language=self.original_language, initial_prompt=init_prompt, verbose=None, condition_on_previous_text=True) return result def ts_words(self,r): # return: transcribe result object to [(beg,end,"word1"), ...] o = [] for s in r["segments"]: for w in s["words"]: t = (w["start"],w["end"],w["text"]) o.append(t) return o def segments_end_ts(self, res): return [s["end"] for s in res["segments"]] class FasterWhisperASR(ASRBase): """Uses faster-whisper library as the backend. Works much faster, appx 4-times (in offline mode). For GPU, it requires installation with a specific CUDNN version. Requires imports, if used: import faster_whisper """ def load_model(self, modelsize, cache_dir): # cache_dir is not set, it seemed not working. Default ~/.cache/huggingface/hub is used. # this worked fast and reliably on NVIDIA L40 model = WhisperModel(modelsize, device="cuda", compute_type="float16") # or run on GPU with INT8 # tested: the transcripts were different, probably worse than with FP16, and it was slightly (appx 20%) slower #model = WhisperModel(model_size, device="cuda", compute_type="int8_float16") # or run on CPU with INT8 # tested: works, but slow, appx 10-times than cuda FP16 #model = WhisperModel(model_size, device="cpu", compute_type="int8") #, download_root="faster-disk-cache-dir/") return model def transcribe(self, audio, init_prompt=""): wt = False segments, info = self.model.transcribe(audio, language=self.original_language, initial_prompt=init_prompt, beam_size=5, word_timestamps=True, condition_on_previous_text=True) return list(segments) def ts_words(self, segments): o = [] for segment in segments: for word in segment.words: # stripping the spaces w = word.word.strip() t = (word.start, word.end, w) o.append(t) return o def segments_end_ts(self, res): return [s.end for s in res] def to_flush(sents, offset=0): # concatenates the timestamped words or sentences into one sequence that is flushed in one line # sents: [(beg1, end1, "sentence1"), ...] or [] if empty # return: (beg1,end-of-last-sentence,"concatenation of sentences") or (None, None, "") if empty t = " ".join(s[2] for s in sents) if len(sents) == 0: b = None e = None else: b = offset + sents[0][0] e = offset + sents[-1][1] return (b,e,t) class HypothesisBuffer: def __init__(self): self.commited_in_buffer = [] self.buffer = [] self.new = [] self.last_commited_time = 0 self.last_commited_word = None def insert(self, new, offset): # compare self.commited_in_buffer and new. It inserts only the words in new that extend the commited_in_buffer, it means they are roughly behind last_commited_time and new in content # the new tail is added to self.new new = [(a+offset,b+offset,t) for a,b,t in new] self.new = [(a,b,t) for a,b,t in new if a > self.last_commited_time-0.1] if len(self.new) >= 1: a,b,t = self.new[0] if abs(a - self.last_commited_time) < 1: if self.commited_in_buffer: # it's going to search for 1, 2 or 3 consecutive words that are identical in commited and new. If they are, they're dropped. cn = len(self.commited_in_buffer) nn = len(self.new) for i in range(1,min(min(cn,nn),5)+1): c = " ".join([self.commited_in_buffer[-j][2] for j in range(1,i+1)][::-1]) tail = " ".join(self.new[j-1][2] for j in range(1,i+1)) if c == tail: print("removing last",i,"words:",file=sys.stderr) for j in range(i): print("\t",self.new.pop(0),file=sys.stderr) break def flush(self): # returns commited chunk = the longest common prefix of 2 last inserts. commit = [] while self.new: na, nb, nt = self.new[0] if len(self.buffer) == 0: break if nt == self.buffer[0][2]: commit.append((na,nb,nt)) self.last_commited_word = nt self.last_commited_time = nb self.buffer.pop(0) self.new.pop(0) else: break self.buffer = self.new self.new = [] self.commited_in_buffer.extend(commit) return commit def pop_commited(self, time): while self.commited_in_buffer and self.commited_in_buffer[0][1] <= time: self.commited_in_buffer.pop(0) def complete(self): return self.buffer class OnlineASRProcessor: SAMPLING_RATE = 16000 def __init__(self, language, asr, chunk): """language: lang. code asr: WhisperASR object chunk: number of seconds for intended size of audio interval that is inserted and looped """ self.language = language self.asr = asr self.tokenizer = MosesTokenizer("en") self.init() self.chunk = chunk def init(self): """run this when starting or restarting processing""" self.audio_buffer = np.array([],dtype=np.float32) self.buffer_time_offset = 0 self.transcript_buffer = HypothesisBuffer() self.commited = [] self.last_chunked_at = 0 self.silence_iters = 0 def insert_audio_chunk(self, audio): self.audio_buffer = np.append(self.audio_buffer, audio) def prompt(self): """Returns a tuple: (prompt, context), where "prompt" is a 200-character suffix of commited text that is inside of the scrolled away part of audio buffer. "context" is the commited text that is inside the audio buffer. It is transcribed again and skipped. It is returned only for debugging and logging reasons. """ k = max(0,len(self.commited)-1) while k > 0 and self.commited[k-1][1] > self.last_chunked_at: k -= 1 p = self.commited[:k] p = [t for _,_,t in p] prompt = [] l = 0 while p and l < 200: # 200 characters prompt size x = p.pop(-1) l += len(x)+1 prompt.append(x) non_prompt = self.commited[k:] return " ".join(prompt[::-1]), " ".join(t for _,_,t in non_prompt) def process_iter(self): """Runs on the current audio buffer. Returns: a tuple (beg_timestamp, end_timestamp, "text"), or (None, None, ""). The non-emty text is confirmed (commited) partial transcript. """ prompt, non_prompt = self.prompt() print("PROMPT:", prompt, file=sys.stderr) print("CONTEXT:", non_prompt, file=sys.stderr) print(f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}",file=sys.stderr) res = self.asr.transcribe(self.audio_buffer, init_prompt=prompt) # transform to [(beg,end,"word1"), ...] tsw = self.asr.ts_words(res) self.transcript_buffer.insert(tsw, self.buffer_time_offset) o = self.transcript_buffer.flush() self.commited.extend(o) print(">>>>COMPLETE NOW:",to_flush(o),file=sys.stderr,flush=True) print("INCOMPLETE:",to_flush(self.transcript_buffer.complete()),file=sys.stderr,flush=True) # there is a newly confirmed text if o: # we trim all the completed sentences from the audio buffer self.chunk_completed_sentence() # ...segments could be considered #self.chunk_completed_segment(res) # # self.silence_iters = 0 # this was an attempt to trim silence/non-linguistic noise detected by the fact that Whisper doesn't transcribe anything for 3-times in a row. # It seemed not working better, or needs to be debugged. # elif self.transcript_buffer.complete(): # self.silence_iters = 0 # elif not self.transcript_buffer.complete(): # # print("NOT COMPLETE:",to_flush(self.transcript_buffer.complete()),file=sys.stderr,flush=True) # self.silence_iters += 1 # if self.silence_iters >= 3: # n = self.last_chunked_at ## self.chunk_completed_sentence() ## if n == self.last_chunked_at: # self.chunk_at(self.last_chunked_at+self.chunk) # print(f"\tCHUNK: 3-times silence! chunk_at {n}+{self.chunk}",file=sys.stderr) ## self.silence_iters = 0 # if the audio buffer is longer than 30s, trim it... if len(self.audio_buffer)/self.SAMPLING_RATE > 30: # ...on the last completed segment (labeled by Whisper) self.chunk_completed_segment(res) # alternative: on any word #l = self.buffer_time_offset + len(self.audio_buffer)/self.SAMPLING_RATE - 10 # let's find commited word that is less #k = len(self.commited)-1 #while k>0 and self.commited[k][1] > l: # k -= 1 #t = self.commited[k][1] print(f"chunking because of len",file=sys.stderr) #self.chunk_at(t) print(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}",file=sys.stderr) return to_flush(o) def chunk_completed_sentence(self): if self.commited == []: return print(self.commited,file=sys.stderr) sents = self.words_to_sentences(self.commited) for s in sents: print("\t\tSENT:",s,file=sys.stderr) if len(sents) < 2: return while len(sents) > 2: sents.pop(0) # we will continue with audio processing at this timestamp chunk_at = sents[-2][1] print(f"--- sentence chunked at {chunk_at:2.2f}",file=sys.stderr) self.chunk_at(chunk_at) def chunk_completed_segment(self, res): if self.commited == []: return ends = self.asr.segments_end_ts(res) t = self.commited[-1][1] if len(ends) > 1: e = ends[-2]+self.buffer_time_offset while len(ends) > 2 and e > t: ends.pop(-1) e = ends[-2]+self.buffer_time_offset if e <= t: print(f"--- segment chunked at {e:2.2f}",file=sys.stderr) self.chunk_at(e) else: print(f"--- last segment not within commited area",file=sys.stderr) else: print(f"--- not enough segments to chunk",file=sys.stderr) def chunk_at(self, time): """trims the hypothesis and audio buffer at "time" """ self.transcript_buffer.pop_commited(time) cut_seconds = time - self.buffer_time_offset self.audio_buffer = self.audio_buffer[int(cut_seconds)*self.SAMPLING_RATE:] self.buffer_time_offset = time self.last_chunked_at = time def words_to_sentences(self, words): """Uses mosestokenizer for sentence segmentation of words. Returns: [(beg,end,"sentence 1"),...] """ cwords = [w for w in words] t = " ".join(o[2] for o in cwords) s = self.tokenizer.split(t) out = [] while s: beg = None end = None sent = s.pop(0).strip() fsent = sent while cwords: b,e,w = cwords.pop(0) if beg is None and sent.startswith(w): beg = b elif end is None and sent == w: end = e out.append((beg,end,fsent)) break sent = sent[len(w):].strip() return out def finish(self): """Flush the incomplete text when the whole processing ends. Returns: the same format as self.process_iter() """ o = self.transcript_buffer.complete() f = to_flush(o) print("last, noncommited:",f,file=sys.stderr) return f ## main: import argparse parser = argparse.ArgumentParser() parser.add_argument('audio_path', type=str, help="Filename of 16kHz mono channel wav, on which live streaming is simulated.") parser.add_argument('--min-chunk-size', type=float, default=1.0, help='Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.') parser.add_argument('--model', type=str, default='large-v2', help="name of the Whisper model to use (default: large-v2, options: {tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large}") parser.add_argument('--model_dir', type=str, default='disk-cache-dir', help="the path where Whisper models are saved (or downloaded to). Default: ./disk-cache-dir") parser.add_argument('--lan', '--language', type=str, default='en', help="Language code for transcription, e.g. en,de,cs.") parser.add_argument('--start_at', type=float, default=0.0, help='Start processing audio at this time.') parser.add_argument('--backend', type=str, default="faster-whisper", choices=["faster-whisper", "whisper_timestamped"],help='Load only this backend for Whisper processing.') args = parser.parse_args() audio_path = args.audio_path SAMPLING_RATE = 16000 duration = len(load_audio(audio_path))/SAMPLING_RATE print("Audio duration is: %2.2f seconds" % duration, file=sys.stderr) size = args.model language = args.lan t = time.time() print(f"Loading Whisper {size} model for {language}...",file=sys.stderr,end=" ",flush=True) #asr = WhisperASR(lan=language, modelsize=size) if args.backend == "faster-whisper": from faster_whisper import WhisperModel asr_cls = FasterWhisperASR 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_dir) e = time.time() print(f"done. It took {round(e-t,2)} seconds.",file=sys.stderr) min_chunk = args.min_chunk_size online = OnlineASRProcessor(language,asr,min_chunk) # load the audio into the LRU cache before we start the timer a = load_audio_chunk(audio_path,0,1) # warm up the ASR, because the very first transcribe takes much more time than the other asr.transcribe(a) def output_transcript(o): # output format in stdout is like: # 4186.3606 0 1720 Takhle to je # - the first three words are: # - emission time from beginning of processing, in milliseconds # - 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 now = time.time()-start if o[0] is not None: print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),flush=True) else: print(o,file=sys.stderr,flush=True) beg = args.start_at end = 0 start = time.time()-beg while True: now = time.time() - start if now < end+min_chunk: time.sleep(min_chunk+end-now) end = time.time() - start a = load_audio_chunk(audio_path,beg,end) beg = end online.insert_audio_chunk(a) try: o = online.process_iter() except AssertionError: print("assertion error",file=sys.stderr) pass else: output_transcript(o) now = time.time() - start print(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}",file=sys.stderr) print(file=sys.stderr,flush=True) if end >= duration: break o = online.finish() output_transcript(o)