def initNnetFeatPipeline(adaptation_state, asr, decodable_opts, feat_info):
feat_pipeline = OnlineNnetFeaturePipeline(feat_info)
feat_pipeline.set_adaptation_state(adaptation_state)
asr.set_input_pipeline(feat_pipeline)
asr.init_decoding()
sil_weighting = OnlineSilenceWeighting(
asr.transition_model, feat_info.silence_weighting_config,
decodable_opts.frame_subsampling_factor)
return feat_pipeline, sil_weighting
def decode_chunked_partial_endpointing(asr,
feat_info,
decodable_opts,
scp,
chunk_size=1024,
compute_confidences=True,
asr_client=None,
speaker="Speaker",
pad_confidences=True):
# Decode (chunked + partial output + endpointing
# + ivector adaptation + silence weighting)
adaptation_state = OnlineIvectorExtractorAdaptationState.from_info(
feat_info.ivector_extractor_info)
for key, wav in SequentialWaveReader(scp):
feat_pipeline = OnlineNnetFeaturePipeline(feat_info)
feat_pipeline.set_adaptation_state(adaptation_state)
asr.set_input_pipeline(feat_pipeline)
asr.init_decoding()
sil_weighting = OnlineSilenceWeighting(
asr.transition_model, feat_info.silence_weighting_config,
decodable_opts.frame_subsampling_factor)
data = wav.data()[0]
print("type(data):", type(data))
last_chunk = False
utt, part = 1, 1
prev_num_frames_decoded, offset = 0, 0
for i in range(0, len(data), chunk_size):
if i + chunk_size >= len(data):
last_chunk = True
feat_pipeline.accept_waveform(wav.samp_freq,
data[i:i + chunk_size])
if last_chunk:
feat_pipeline.input_finished()
if sil_weighting.active():
sil_weighting.compute_current_traceback(asr.decoder)
feat_pipeline.ivector_feature().update_frame_weights(
sil_weighting.get_delta_weights(
feat_pipeline.num_frames_ready()))
asr.advance_decoding()
num_frames_decoded = asr.decoder.num_frames_decoded()
if not last_chunk:
if asr.endpoint_detected():
asr.finalize_decoding()
out = asr.get_output()
mbr = MinimumBayesRisk(out["lattice"])
confd = mbr.get_one_best_confidences()
if pad_confidences:
token_length = len(out["text"].split())
# computed confidences array is smaller than the actual token length,
if len(confd) < token_length:
print(
"WARNING: less computeted confidences than token length! Fixing this with padding!"
)
confd = np.pad(confd,
[0, token_length - len(confd)],
mode='constant',
constant_values=1.0)
elif len(confd) > token_length:
print(
"WARNING: more computeted confidences than token length! Fixing this with slicing!"
)
confd = confd[:token_length]
print(confd)
# print(key + "-utt%d-final" % utt, out["text"], flush=True)
if asr_client is not None:
asr_client.completeUtterance(
utterance=out["text"],
key=key + "-utt%d-part%d" % (utt, part),
confidences=confd)
offset += int(num_frames_decoded *
decodable_opts.frame_subsampling_factor *
feat_pipeline.frame_shift_in_seconds() *
wav.samp_freq)
feat_pipeline.get_adaptation_state(adaptation_state)
feat_pipeline = OnlineNnetFeaturePipeline(feat_info)
feat_pipeline.set_adaptation_state(adaptation_state)
asr.set_input_pipeline(feat_pipeline)
asr.init_decoding()
sil_weighting = OnlineSilenceWeighting(
asr.transition_model,
feat_info.silence_weighting_config,
decodable_opts.frame_subsampling_factor)
remainder = data[offset:i + chunk_size]
feat_pipeline.accept_waveform(wav.samp_freq, remainder)
utt += 1
part = 1
prev_num_frames_decoded = 0
elif num_frames_decoded > prev_num_frames_decoded:
prev_num_frames_decoded = num_frames_decoded
out = asr.get_partial_output()
# print(key + "-utt%d-part%d" % (utt, part),
# out["text"], flush=True)
if asr_client is not None:
asr_client.partialUtterance(utterance=out["text"],
key=key + "-utt%d-part%d" %
(utt, part))
part += 1
asr.finalize_decoding()
out = asr.get_output()
mbr = MinimumBayesRisk(out["lattice"])
confd = mbr.get_one_best_confidences()
print(out)
# print(key + "-utt%d-final" % utt, out["text"], flush=True)
if asr_client is not None:
asr_client.completeUtterance(utterance=out["text"],
key=key + "-utt%d-part%d" %
(utt, part),
confidences=confd)
feat_pipeline.get_adaptation_state(adaptation_state)
part += 1
asr.finalize_decoding()
out = asr.get_output()
print(key + "-final", out["text"], flush=True)
# Decode (chunked + partial output + endpointing
# + ivector adaptation + silence weighting)
adaptation_state = OnlineIvectorExtractorAdaptationState.from_info(
feat_info.ivector_extractor_info)
for key, wav in SequentialWaveReader("scp:wav.scp"):
feat_pipeline = OnlineNnetFeaturePipeline(feat_info)
feat_pipeline.set_adaptation_state(adaptation_state)
asr.set_input_pipeline(feat_pipeline)
asr.init_decoding()
sil_weighting = OnlineSilenceWeighting(
asr.transition_model, feat_info.silence_weighting_config,
decodable_opts.frame_subsampling_factor)
data = wav.data()[0]
last_chunk = False
utt, part = 1, 1
prev_num_frames_decoded, offset = 0, 0
for i in range(0, len(data), chunk_size):
if i + chunk_size >= len(data):
last_chunk = True
feat_pipeline.accept_waveform(wav.samp_freq, data[i:i + chunk_size])
if last_chunk:
feat_pipeline.input_finished()
if sil_weighting.active():
sil_weighting.compute_current_traceback(asr.decoder)
feat_pipeline.ivector_feature().update_frame_weights(
sil_weighting.get_delta_weights(