whisper_online.py

#!/usr/bin/env python3
import sys
import numpy as np
import librosa  
from functools import lru_cache
import time
import datetime


@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:

    sep = " "   # join transcribe words with this character (" " for whisper_timestamped,
                # "" for faster-whisper because it emits the spaces when neeeded)

    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
        self.logfile = logfile

        self.transcribe_kargs = {}
        self.original_language = lan 

        self.model = self.load_model(modelsize, cache_dir, model_dir)


    def load_model(self, modelsize, cache_dir):
        raise NotImplemented("must be implemented in the child class")

    def transcribe(self, audio, init_prompt=""):
        raise NotImplemented("must be implemented in the child class")

    def use_vad(self):
        raise NotImplemented("must be implemented in the child class")


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.
    """

    sep = " "

    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
        import whisper
        from whisper_timestamped import transcribe_timestamped
        self.transcribe_timestamped = transcribe_timestamped
        if model_dir is not None:
            print("ignoring model_dir, not implemented",file=self.logfile)
        return whisper.load_model(modelsize, download_root=cache_dir)

    def transcribe(self, audio, init_prompt=""):
        result = self.transcribe_timestamped(self.model,
                audio, language=self.original_language,
                initial_prompt=init_prompt, verbose=None,
                condition_on_previous_text=True, **self.transcribe_kargs)
        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"]]

    def use_vad(self):
        self.transcribe_kargs["vad"] = True

    def set_translate_task(self):
        self.transcribe_kargs["task"] = "translate"




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.
    """

    sep = ""

    def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
        from faster_whisper import WhisperModel
        if model_dir is not None:
            print(f"Loading whisper model from model_dir {model_dir}. modelsize and cache_dir parameters are not used.",file=self.logfile)
            model_size_or_path = model_dir
        elif modelsize is not None:
            model_size_or_path = modelsize
        else:
            raise ValueError("modelsize or model_dir parameter must be set")


        # this worked fast and reliably on NVIDIA L40
        model = WhisperModel(model_size_or_path, device="cuda", compute_type="float16", download_root=cache_dir)

        # 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(modelsize, device="cpu", compute_type="int8") #, download_root="faster-disk-cache-dir/")
        return model

    def transcribe(self, audio, init_prompt=""):

        # tiempo_inicio = datetime.datetime.now()
        # tested: beam_size=5 is faster and better than 1 (on one 200 second document from En ESIC, min chunk 0.01)
        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, **self.transcribe_kargs)
        
        # print(f'({datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")})----------r> whisper transcribe  take { (datetime.datetime.now() -tiempo_inicio)  } ms.')

        return list(segments)

    def ts_words(self, segments):
        o = []
        for segment in segments:
            for word in segment.words:
                if segment.no_speech_prob > 0.9:
                    continue
                # not stripping the spaces -- should not be merged with them!
                w = word.word
                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 use_vad(self):
        self.transcribe_kargs["vad_filter"] = True

    def set_translate_task(self):
        self.transcribe_kargs["task"] = "translate"



class HypothesisBuffer:

    def __init__(self, logfile=sys.stderr):
        self.commited_in_buffer = []
        self.buffer = []
        self.new = []

        self.last_commited_time = 0
        self.last_commited_word = None

        self.logfile = logfile

    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, ..., 5 consecutive words (n-grams) 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):  # 5 is the maximum 
                        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=self.logfile)
                            for j in range(i):
                                print("\t",self.new.pop(0),file=self.logfile)
                            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, asr, tokenizer, logfile=sys.stderr):
        """asr: WhisperASR object
        tokenizer: sentence tokenizer object for the target language. Must have a method *split* that behaves like the one of MosesTokenizer.
        logfile: where to store the log. 
        """
        self.asr = asr
        self.tokenizer = tokenizer
        self.logfile = logfile

        self.init()

    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(logfile=self.logfile)
        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 self.asr.sep.join(prompt[::-1]), self.asr.sep.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 (committed) partial transcript.
        """

        prompt, non_prompt = self.prompt()
        print("PROMPT:", prompt, file=self.logfile)
        print("CONTEXT:", non_prompt, file=self.logfile)
        print(f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}",file=self.logfile)
        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:",self.to_flush(o),file=self.logfile,flush=True)
        print("INCOMPLETE:",self.to_flush(self.transcript_buffer.complete()),file=self.logfile,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=self.logfile,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=self.logfile)
##                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=self.logfile)
            #self.chunk_at(t)

        print(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}",file=self.logfile)
        return self.to_flush(o)

    def chunk_completed_sentence(self):
        if self.commited == []: return
        print(self.commited,file=self.logfile)
        sents = self.words_to_sentences(self.commited)
        for s in sents:
            print("\t\tSENT:",s,file=self.logfile)
        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=self.logfile)
        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=self.logfile)
                self.chunk_at(e)
            else:
                print(f"--- last segment not within commited area",file=self.logfile)
        else:
            print(f"--- not enough segments to chunk",file=self.logfile)





    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 self.tokenizer 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 = self.to_flush(o)
        print("last, noncommited:",f,file=self.logfile)
        return f


    def to_flush(self, sents, sep=None, 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
        if sep is None:
            sep = self.asr.sep
        t = sep.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)

WHISPER_LANG_CODES = "af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,he,hi,hr,ht,hu,hy,id,is,it,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,zh".split(",")

def create_tokenizer(lan):
    """returns an object that has split function that works like the one of MosesTokenizer"""

    assert lan in WHISPER_LANG_CODES, "language must be Whisper's supported lang code: " + " ".join(WHISPER_LANG_CODES)

    if lan == "uk":
        import tokenize_uk
        class UkrainianTokenizer:
            def split(self, text):
                return tokenize_uk.tokenize_sents(text)
        return UkrainianTokenizer()

    # supported by fast-mosestokenizer
    if lan in "as bn ca cs de el en es et fi fr ga gu hi hu is it kn lt lv ml mni mr nl or pa pl pt ro ru sk sl sv ta te yue zh".split():
        from mosestokenizer import MosesTokenizer
        return MosesTokenizer(lan)

    # the following languages are in Whisper, but not in wtpsplit:
    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():
        print(f"{lan} code is not supported by wtpsplit. Going to use None lang_code option.", file=sys.stderr)
        lan = None

    from wtpsplit import WtP
    # downloads the model from huggingface on the first use
    wtp = WtP("wtp-canine-s-12l-no-adapters")
    class WtPtok:
        def split(self, sent):
            return wtp.split(sent, lang_code=lan)
    return WtPtok()




## main:

if __name__ == "__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', choices="tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large".split(","),help="Name size of the Whisper model to use (default: large-v2). The model is automatically downloaded from the model hub if not present in model cache dir.")
    parser.add_argument('--model_cache_dir', type=str, default=None, help="Overriding the default model cache dir where models downloaded from the hub are saved")
    parser.add_argument('--model_dir', type=str, default=None, help="Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.")
    parser.add_argument('--lan', '--language', type=str, default='en', help="Language code for transcription, e.g. en,de,cs.")
    parser.add_argument('--task', type=str, default='transcribe', choices=["transcribe","translate"],help="Transcribe or translate.")
    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.')
    parser.add_argument('--offline', action="store_true", default=False, help='Offline mode.')
    parser.add_argument('--comp_unaware', action="store_true", default=False, help='Computationally unaware simulation.')
    parser.add_argument('--vad', action="store_true", default=False, help='Use VAD = voice activity detection, with the default parameters.')
    args = parser.parse_args()

    # reset to store stderr to different file stream, e.g. open(os.devnull,"w")
    logfile = sys.stderr

    if args.offline and args.comp_unaware:
        print("No or one option from --offline and --comp_unaware are available, not both. Exiting.",file=logfile)
        sys.exit(1)

    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=logfile)

    size = args.model
    language = args.lan

    t = time.time()
    print(f"Loading Whisper {size} model for {language}...",file=logfile,end=" ",flush=True)

    if args.backend == "faster-whisper":
        asr_cls = FasterWhisperASR
    else:
        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"  # Whisper translates into English
    else:
        tgt_language = language  # Whisper transcribes in this language


    e = time.time()
    print(f"done. It took {round(e-t,2)} seconds.",file=logfile)

    if args.vad:
        print("setting VAD filter",file=logfile)
        asr.use_vad()

    
    min_chunk = args.min_chunk_size
    online = OnlineASRProcessor(asr,create_tokenizer(tgt_language),logfile=logfile)


    # 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)

    beg = args.start_at
    start = time.time()-beg

    def output_transcript(o, now=None):
        # 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
        if now is None:
            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]),file=logfile,flush=True)
            print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),flush=True)
        else:
            print(o,file=logfile,flush=True)

    if args.offline: ## offline mode processing (for testing/debugging)
        a = load_audio(audio_path)
        online.insert_audio_chunk(a)
        try:
            o = online.process_iter()
        except AssertionError:
            print("assertion error",file=logfile)
            pass
        else:
            output_transcript(o)
        now = None
    elif args.comp_unaware:  # computational unaware mode 
        end = beg + min_chunk
        while True:
            a = load_audio_chunk(audio_path,beg,end)
            online.insert_audio_chunk(a)
            try:
                o = online.process_iter()
            except AssertionError:
                print("assertion error",file=logfile)
                pass
            else:
                output_transcript(o, now=end)

            print(f"## last processed {end:.2f}s",file=logfile,flush=True)

            if end >= duration:
                break
            
            beg = end
            
            if end + min_chunk > duration:
                end = duration
            else:
                end += min_chunk
        now = duration

    else: # online = simultaneous mode
        end = 0
        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=logfile)
                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=logfile,flush=True)

            if end >= duration:
                break
        now = None

    o = online.finish()
    output_transcript(o, now=now)