def run_evaluation(
test_loader,
model,
decoder: Decoder,
device: torch.device,
target_decoder: Decoder,
# precision: Precision
):
model.eval()
wer = WordErrorRate(decoder=decoder, target_decoder=target_decoder)
cer = CharErrorRate(decoder=decoder, target_decoder=target_decoder)
for i, (batch) in tqdm(enumerate(test_loader), total=len(test_loader)):
inputs, targets, input_percentages, target_sizes = batch
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
inputs = inputs.to(device)
# with autocast(enabled=precision is Precision.half):
with autocast(enabled=True):
out, output_sizes = model(inputs, input_sizes)
decoded_output, _ = decoder.decode(out, output_sizes)
wer.update(preds=out,
preds_sizes=output_sizes,
targets=targets,
target_sizes=target_sizes)
cer.update(preds=out,
preds_sizes=output_sizes,
targets=targets,
target_sizes=target_sizes)
return wer.compute(), cer.compute()
def run_transcribe(audio_path: str, spect_parser: SpectrogramParser,
model: DeepSpeech, decoder: Decoder, device: torch.device,
use_half: bool):
# audio_path
# try:
# # inTranscript = audio_path.replace("wav", "txt")
# # print(inTranscript)
# # getTranscript(inTranscript)
# pass
# except Exception as asd:
# print(asd)
# pass
spect = spect_parser.parse_audio(audio_path).contiguous()
spect = spect.view(1, 1, spect.size(0), spect.size(1))
spect = spect.to(device)
if use_half:
spect = spect.half()
input_sizes = torch.IntTensor([spect.size(3)]).int()
out, output_sizes = model(spect, input_sizes)
decoded_output, decoded_offsets = decoder.decode(out, output_sizes)
#Thêm vào greedy
decoder2 = GreedyDecoder(labels=model.labels,
blank_index=model.labels.index('_'))
decoded_output2, decoded_offsets2 = decoder2.decode(out, output_sizes)
return decoded_output, decoded_output2, decoded_offsets, decoded_offsets2
def run_transcribe(audio_path: str, spect_parser: SpectrogramParser,
model: DeepSpeech, decoder: Decoder, device: torch.device,
precision: int):
spect = spect_parser.parse_audio(audio_path).contiguous()
spect = spect.view(1, 1, spect.size(0), spect.size(1))
spect = spect.to(device)
input_sizes = torch.IntTensor([spect.size(3)]).int()
with autocast(enabled=precision == 16):
out, output_sizes = model(spect, input_sizes)
decoded_output, decoded_offsets = decoder.decode(out, output_sizes)
return decoded_output, decoded_offsets
def run_transcribe(audio_path: str, spect_parser: SpectrogramParser,
model: DeepSpeech, decoder: Decoder, device: torch.device,
use_half: bool):
spect = spect_parser.parse_audio(audio_path).contiguous()
spect = spect.view(1, 1, spect.size(0), spect.size(1))
spect = spect.to(device)
if use_half:
spect = spect.half()
input_sizes = torch.IntTensor([spect.size(3)]).int()
out, output_sizes = model(spect, input_sizes)
decoded_output, decoded_offsets = decoder.decode(out, output_sizes)
return decoded_output, decoded_offsets
def run_evaluation_print(test_loader,
model,
decoder: Decoder,
device: torch.device,
target_decoder: Decoder,
precision: int):
# track time to complete
start_time = time.time()
model.eval()
wer = WordErrorRate(
decoder=decoder,
target_decoder=target_decoder
)
cer = CharErrorRate(
decoder=decoder,
target_decoder=target_decoder
)
output_dict = {}
pred, gt = [],[]
for i, (batch) in tqdm(enumerate(test_loader), total=len(test_loader)):
inputs, targets, input_percentages, target_sizes = batch
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
inputs = inputs.to(device)
with autocast(enabled=precision == 16):
out, output_sizes = model(inputs, input_sizes)
decoded_output, _ = decoder.decode(out, output_sizes)
# prints out text
# unflatten targets
split_targets = []
offset = 0
for size in target_sizes:
split_targets.append(targets[offset:offset + size])
offset += size
#print('split_targets:')
#print(split_targets)
target_strings = target_decoder.convert_to_strings(split_targets)
#print('target_strings:')
#print(target_strings)
for x in range(len(target_strings)):
transcript, reference = decoded_output[x][0], target_strings[x][0]
pred.append(transcript)
gt.append(reference)
# self.calculate_metric(
# transcript=transcript,
# reference=reference
# )
wer.update(
preds=out,
preds_sizes=output_sizes,
targets=targets,
target_sizes=target_sizes
)
cer.update(
preds=out,
preds_sizes=output_sizes,
targets=targets,
target_sizes=target_sizes
)
output_dict['pred'] = pred
output_dict['gt'] = gt
print("--- Time taken to infer %s seconds ---" % (time.time() - start_time))
output_file_path = '/root/deepspeech/outputs/valid_output.json'
#export to file
with open(output_file_path, 'w') as fp:
json.dump(output_dict, fp)
return wer.compute(), cer.compute()