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add option to save log to file

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This commit is contained in:
Luca
2023-09-06 15:19:12 +02:00
parent c0dd2e2db9
commit 6e6b619257
1 changed files with 29 additions and 39 deletions
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68
whisper_online.py
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@@ -46,10 +46,6 @@ class ASRBase:
raise NotImplemented("must 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.
@@ -64,7 +60,7 @@ class WhisperTimestampedASR(ASRBase):
def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
if model_dir is not None:
print("ignoring model_dir, not implemented",file=sys.stderr)
print("ignoring model_dir, not implemented",file=self.output)
return whisper.load_model(modelsize, download_root=cache_dir)
def transcribe(self, audio, init_prompt=""):
@@ -89,9 +85,6 @@ class WhisperTimestampedASR(ASRBase):
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
"""
sep = ""
@@ -101,11 +94,8 @@ class FasterWhisperASR(ASRBase):
import faster_whisper
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=sys.stderr)
print(f"Loading whisper model from model_dir {model_dir}. modelsize and cache_dir parameters are not used.",file=self.output)
model_size_or_path = model_dir
elif modelsize is not None:
model_size_or_path = modelsize
@@ -153,7 +143,8 @@ class FasterWhisperASR(ASRBase):
class HypothesisBuffer:
def __init__(self):
def __init__(self, output=sys.stderr):
"""output: where to store the log. Leave it unchanged to print to terminal."""
self.commited_in_buffer = []
self.buffer = []
self.new = []
@@ -161,6 +152,8 @@ class HypothesisBuffer:
self.last_commited_time = 0
self.last_commited_word = None
self.output = output
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
@@ -179,9 +172,9 @@ class HypothesisBuffer:
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)
print("removing last",i,"words:",file=self.output)
for j in range(i):
print("\t",self.new.pop(0),file=sys.stderr)
print("\t",self.new.pop(0),file=self.output)
break
def flush(self):
@@ -218,12 +211,14 @@ class OnlineASRProcessor:
SAMPLING_RATE = 16000
def __init__(self, asr, tokenizer):
def __init__(self, asr, tokenizer, output=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.
output: where to store the log. Leave it unchanged to print to terminal.
"""
self.asr = asr
self.tokenizer = tokenizer
self.output = output
self.init()
@@ -232,7 +227,7 @@ class OnlineASRProcessor:
self.audio_buffer = np.array([],dtype=np.float32)
self.buffer_time_offset = 0
self.transcript_buffer = HypothesisBuffer()
self.transcript_buffer = HypothesisBuffer(output=self.output)
self.commited = []
self.last_chunked_at = 0
@@ -263,13 +258,13 @@ class OnlineASRProcessor:
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.
The non-emty text is confirmed (committed) 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)
print("PROMPT:", prompt, file=self.output)
print("CONTEXT:", non_prompt, file=self.output)
print(f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}",file=self.output)
res = self.asr.transcribe(self.audio_buffer, init_prompt=prompt)
# transform to [(beg,end,"word1"), ...]
@@ -278,8 +273,8 @@ class OnlineASRProcessor:
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=sys.stderr,flush=True)
print("INCOMPLETE:",self.to_flush(self.transcript_buffer.complete()),file=sys.stderr,flush=True)
print(">>>>COMPLETE NOW:",self.to_flush(o),file=self.output,flush=True)
print("INCOMPLETE:",self.to_flush(self.transcript_buffer.complete()),file=self.output,flush=True)
# there is a newly confirmed text
if o:
@@ -298,14 +293,14 @@ class OnlineASRProcessor:
# 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)
# # print("NOT COMPLETE:",to_flush(self.transcript_buffer.complete()),file=self.output,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)
# print(f"\tCHUNK: 3-times silence! chunk_at {n}+{self.chunk}",file=self.output)
## self.silence_iters = 0
@@ -321,18 +316,18 @@ class OnlineASRProcessor:
#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)
print(f"chunking because of len",file=self.output)
#self.chunk_at(t)
print(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}",file=sys.stderr)
print(f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}",file=self.output)
return self.to_flush(o)
def chunk_completed_sentence(self):
if self.commited == []: return
print(self.commited,file=sys.stderr)
print(self.commited,file=self.output)
sents = self.words_to_sentences(self.commited)
for s in sents:
print("\t\tSENT:",s,file=sys.stderr)
print("\t\tSENT:",s,file=self.output)
if len(sents) < 2:
return
while len(sents) > 2:
@@ -340,7 +335,7 @@ class OnlineASRProcessor:
# 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)
print(f"--- sentence chunked at {chunk_at:2.2f}",file=self.output)
self.chunk_at(chunk_at)
def chunk_completed_segment(self, res):
@@ -357,12 +352,12 @@ class OnlineASRProcessor:
ends.pop(-1)
e = ends[-2]+self.buffer_time_offset
if e <= t:
print(f"--- segment chunked at {e:2.2f}",file=sys.stderr)
print(f"--- segment chunked at {e:2.2f}",file=self.output)
self.chunk_at(e)
else:
print(f"--- last segment not within commited area",file=sys.stderr)
print(f"--- last segment not within commited area",file=self.output)
else:
print(f"--- not enough segments to chunk",file=sys.stderr)
print(f"--- not enough segments to chunk",file=self.output)
@@ -408,7 +403,7 @@ class OnlineASRProcessor:
"""
o = self.transcript_buffer.complete()
f = self.to_flush(o)
print("last, noncommited:",f,file=sys.stderr)
print("last, noncommited:",f,file=self.output)
return f
@@ -473,15 +468,10 @@ if __name__ == "__main__":
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_cache_dir, model_dir=args.model_dir)