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#!/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)