1. refactored wav chunk processing method
2. renamed streamlit to validation_ui
parent
d87369c8fe
commit
de21952349
|
|
@ -34,7 +34,8 @@ def extract_data(
|
|||
events = ExtendedPath(meta_path).read_json()
|
||||
yield call_wav, wav_path, events
|
||||
else:
|
||||
typer.echo(f"missing json corresponding to {wav_path}")
|
||||
if verbose:
|
||||
typer.echo(f"missing json corresponding to {wav_path}")
|
||||
|
||||
def contains_asr(x):
|
||||
return "AsrResult" in x
|
||||
|
|
@ -55,6 +56,19 @@ def extract_data(
|
|||
- datetime.datetime(1900, 1, 1)
|
||||
).total_seconds() * 1000
|
||||
|
||||
def process_utterance_chunk(wav_seg, start_time, end_time, monologue):
|
||||
# offset by 1sec left side to include vad? discarded audio
|
||||
full_tscript_wav_seg = wav_seg[
|
||||
time_to_msecs(start_time) - 1000 : time_to_msecs(end_time) # + 1000
|
||||
]
|
||||
tscript_wav_seg = strip_silence(full_tscript_wav_seg)
|
||||
tscript_wav_fb = BytesIO()
|
||||
tscript_wav_seg.export(tscript_wav_fb, format="wav")
|
||||
tscript_wav = tscript_wav_fb.getvalue()
|
||||
text = "".join(lens["elements"].Each()["value"].collect()(monologue))
|
||||
text_clean = re.sub(r"\[.*\]", "", text)
|
||||
return tscript_wav, tscript_wav_seg.duration_seconds, text_clean
|
||||
|
||||
def dual_asr_data_generator(wav_seg, wav_path, meta):
|
||||
left_audio, right_audio = wav_seg.split_to_mono()
|
||||
channel_map = {"Agent": right_audio, "Client": left_audio}
|
||||
|
|
@ -64,7 +78,9 @@ def extract_data(
|
|||
speaker_channel = channel_map.get(monologue["speaker_name"])
|
||||
if not speaker_channel:
|
||||
if verbose:
|
||||
print(f'unknown speaker tag {monologue["speaker_name"]} in wav:{wav_path} skipping.')
|
||||
print(
|
||||
f'unknown speaker tag {monologue["speaker_name"]} in wav:{wav_path} skipping.'
|
||||
)
|
||||
continue
|
||||
try:
|
||||
start_time = (
|
||||
|
|
@ -81,23 +97,20 @@ def extract_data(
|
|||
)
|
||||
except IndexError:
|
||||
if verbose:
|
||||
print(f'error when loading timestamp events in wav:{wav_path} skipping.')
|
||||
print(
|
||||
f"error when loading timestamp events in wav:{wav_path} skipping."
|
||||
)
|
||||
continue
|
||||
|
||||
# offset by 500 msec to include first vad? discarded audio
|
||||
full_tscript_wav_seg = speaker_channel[time_to_msecs(start_time) - 500 : time_to_msecs(end_time)]
|
||||
tscript_wav_seg = strip_silence(full_tscript_wav_seg)
|
||||
tscript_wav_fb = BytesIO()
|
||||
tscript_wav_seg.export(tscript_wav_fb, format="wav")
|
||||
tscript_wav = tscript_wav_fb.getvalue()
|
||||
text = "".join(lens["elements"].Each()["value"].collect()(monologue))
|
||||
text_clean = re.sub(r"\[.*\]", "", text)
|
||||
# only if some reasonable audio data is present yield it
|
||||
if tscript_wav_seg.duration_seconds < 0.5:
|
||||
tscript_wav, seg_dur, text_clean = process_utterance_chunk(
|
||||
speaker_channel, start_time, end_time, monologue
|
||||
)
|
||||
if seg_dur < 0.5:
|
||||
if verbose:
|
||||
print(f'transcript chunk "{text_clean}" contains no audio in {wav_path} skipping.')
|
||||
print(
|
||||
f'transcript chunk "{text_clean}" contains no audio in {wav_path} skipping.'
|
||||
)
|
||||
continue
|
||||
yield text_clean, tscript_wav_seg.duration_seconds, tscript_wav
|
||||
yield text_clean, seg_dur, tscript_wav
|
||||
|
||||
def mono_asr_data_generator(wav_seg, wav_path, meta):
|
||||
monologues = lens["monologues"].Each().collect()(meta)
|
||||
|
|
@ -117,30 +130,33 @@ def extract_data(
|
|||
)
|
||||
except IndexError:
|
||||
if verbose:
|
||||
print(f'error when loading timestamp events in wav:{wav_path} skipping.')
|
||||
print(
|
||||
f"error when loading timestamp events in wav:{wav_path} skipping."
|
||||
)
|
||||
continue
|
||||
|
||||
# offset by 500 msec to include first vad? discarded audio
|
||||
full_tscript_wav_seg = wav_seg[time_to_msecs(start_time) - 500 : time_to_msecs(end_time)]
|
||||
tscript_wav_seg = strip_silence(full_tscript_wav_seg)
|
||||
tscript_wav_fb = BytesIO()
|
||||
tscript_wav_seg.export(tscript_wav_fb, format="wav")
|
||||
tscript_wav = tscript_wav_fb.getvalue()
|
||||
text = "".join(lens["elements"].Each()["value"].collect()(monologue))
|
||||
text_clean = re.sub(r"\[.*\]", "", text)
|
||||
if tscript_wav_seg.duration_seconds < 0.5:
|
||||
tscript_wav, seg_dur, text_clean = process_utterance_chunk(
|
||||
wav_seg, start_time, end_time, monologue
|
||||
)
|
||||
if seg_dur < 0.5:
|
||||
if verbose:
|
||||
print(f'transcript chunk "{text_clean}" contains no audio in {wav_path} skipping.')
|
||||
print(
|
||||
f'transcript chunk "{text_clean}" contains no audio in {wav_path} skipping.'
|
||||
)
|
||||
continue
|
||||
yield text_clean, tscript_wav_seg.duration_seconds, tscript_wav
|
||||
yield text_clean, seg_dur, tscript_wav
|
||||
|
||||
def generate_rev_asr_data():
|
||||
full_asr_data = []
|
||||
total_duration = 0
|
||||
for wav, wav_path, ev in wav_event_generator(call_audio_dir):
|
||||
if wav.channels > 2:
|
||||
print(f'skipping many channel audio {wav_path}')
|
||||
asr_data_generator = mono_asr_data_generator if wav.channels == 1 else dual_asr_data_generator
|
||||
print(f"skipping many channel audio {wav_path}")
|
||||
asr_data_generator = (
|
||||
mono_asr_data_generator
|
||||
if wav.channels == 1
|
||||
else dual_asr_data_generator
|
||||
)
|
||||
asr_data = asr_data_generator(wav, wav_path, ev)
|
||||
total_duration += wav.duration_seconds
|
||||
full_asr_data.append(asr_data)
|
||||
|
|
|
|||
|
|
@ -122,7 +122,7 @@ def dump_validation_ui_data(
|
|||
),
|
||||
)
|
||||
if annotation_only:
|
||||
result = pnr_data
|
||||
result = list(pnr_data)
|
||||
else:
|
||||
wer_key = "domain_wer" if use_domain_asr else "pretrained_wer"
|
||||
result = sorted(pnr_data, key=lambda x: x[wer_key], reverse=True)
|
||||
|
|
|
|||
Loading…
Reference in New Issue