def __init__(self, model_path):
"""
:param model_path:
"""
assert os.path.exists(model_path), "Cannot find model here {}".format(
model_path)
self.deep_speech_model = DeepSpeech.load_model(model_path)
self.deep_speech_model.eval()
labels = DeepSpeech.get_labels(self.deep_speech_model)
self.audio_conf = DeepSpeech.get_audio_conf(self.deep_speech_model)
self.decoder = GreedyDecoder(labels)
self.parser = SpectrogramParser(self.audio_conf, normalize=True)
def main():
import argparse
global model, spect_parser, decoder, args
parser = argparse.ArgumentParser(description='DeepSpeech transcription server')
parser.add_argument('--host', type=str, default='0.0.0.0', help='Host to be used by the server')
parser.add_argument('--port', type=int, default=8888, help='Port to be used by the server')
parser = add_inference_args(parser)
parser = add_decoder_args(parser)
args = parser.parse_args()
logging.getLogger().setLevel(logging.DEBUG)
logging.info('Setting up server...')
torch.set_grad_enabled(False)
model = DeepSpeech.load_model(args.model_path)
if args.cuda:
model.cuda()
model.eval()
labels = DeepSpeech.get_labels(model)
audio_conf = DeepSpeech.get_audio_conf(model)
if args.decoder == "beam":
from decoder import BeamCTCDecoder
decoder = BeamCTCDecoder(labels, lm_path=args.lm_path, alpha=args.alpha, beta=args.beta,
cutoff_top_n=args.cutoff_top_n, cutoff_prob=args.cutoff_prob,
beam_width=args.beam_width, num_processes=args.lm_workers)
else:
decoder = GreedyDecoder(labels, blank_index=labels.index('_'))
spect_parser = SpectrogramParser(audio_conf, normalize=True)
logging.info('Server initialised')
app.run(host=args.host, port=args.port, debug=True, use_reloader=False)
class SpeechTranscriber:
def __init__(self, model_path):
"""
:param model_path:
"""
assert os.path.exists(model_path), "Cannot find model here {}".format(
model_path)
self.deep_speech_model = DeepSpeech.load_model(model_path)
self.deep_speech_model.eval()
labels = DeepSpeech.get_labels(self.deep_speech_model)
self.audio_conf = DeepSpeech.get_audio_conf(self.deep_speech_model)
self.decoder = GreedyDecoder(labels)
self.parser = SpectrogramParser(self.audio_conf, normalize=True)
def transcribe(self, audio_file):
"""
:param audio_file:
:return:
"""
spect = self.parser.parse_audio(audio_file).contiguous()
spect = spect.view(1, 1, spect.size(0), spect.size(1))
out = self.deep_speech_model(Variable(spect, volatile=True))
out = out.transpose(0, 1) # TxNxH
decoded_output = self.decoder.decode(out.data)
return decoded_output
def build():
global model, spect_parser, decoder, device
logging.getLogger().setLevel(logging.DEBUG)
logging.info('Setting up server...')
torch.set_grad_enabled(False)
device = torch.device("cpu")
model = load_model(device, "/workspace/models/deepspeech_final.pth", False)
decoder = GreedyDecoder(model.labels, blank_index=model.labels.index('_'))
spect_parser = SpectrogramParser(model.audio_conf, normalize=True)
logging.info('Server initialised')
def __init__(self, *args, **kwargs):
super(Tester, self).__init__(*args, **kwargs)
device = 'cpu'
model_path = '/home/chris/git/deepspeech.pytorch/models/ted_pretrained_v2.pth'
half = False
model = load_model(device, model_path, half)
# if args.decoder == "beam":
# from decoder import BeamCTCDecoder
# decoder = BeamCTCDecoder(model.labels, lm_path=args.lm_path, alpha=args.alpha, beta=args.beta,
# cutoff_top_n=args.cutoff_top_n, cutoff_prob=args.cutoff_prob,
# beam_width=args.beam_width, num_processes=args.lm_workers)
# else:
decoder = GreedyDecoder(model.labels, blank_index=model.labels.index('_'))
self.half = half
self.device = device
self.decoder = decoder
self.model = model
self.spect_parser = SpectrogramParser(model.audio_conf, normalize=True)
spect = spect.to(device)
input_sizes = torch.IntTensor([1]).int()
out, output_sizes = model(spect, input_sizes)
return out_to_preds(out)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='DeepSpeech transcription')
parser = add_inference_args(parser)
parser.add_argument('--audio-path', default='audio.wav',
help='Audio file to predict on')
parser.add_argument('--offsets', dest='offsets', action='store_true', help='Returns time offset information')
parser = add_decoder_args(parser)
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
model = load_model(device, args.model_path, args.cuda)
if args.decoder == "beam":
from decoder import BeamCTCDecoder
decoder = BeamCTCDecoder(model.labels, lm_path=args.lm_path, alpha=args.alpha, beta=args.beta,
cutoff_top_n=args.cutoff_top_n, cutoff_prob=args.cutoff_prob,
beam_width=args.beam_width, num_processes=args.lm_workers)
else:
decoder = GreedyDecoder(model.labels, blank_index=model.labels.index('_'))
parser = SpectrogramParser(model.audio_conf, normalize=True)
decoded_output, decoded_offsets = transcribe(args.audio_path, parser, model, decoder, device)
print(json.dumps(decode_results(model, decoded_output, decoded_offsets)))
default='hamming',
help='Window type for spectrogram generation')
parser.add_argument('--cuda',
default=True,
type=bool,
help='Use cuda to train model')
args = parser.parse_args()
if __name__ == '__main__':
package = torch.load(args.model_path)
model = DeepSpeech(rnn_hidden_size=package['hidden_size'],
nb_layers=package['hidden_layers'],
num_classes=package['nout'])
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(package['state_dict'])
audio_conf = dict(sample_rate=args.sample_rate,
window_size=args.window_size,
window_stride=args.window_stride,
window=args.window)
with open(args.labels_path) as label_file:
labels = str(''.join(json.load(label_file)))
decoder = ArgMaxDecoder(labels)
parser = SpectrogramParser(audio_conf, normalize=True)
spect = parser.parse_audio(args.audio_path).contiguous()
spect = spect.view(1, 1, spect.size(0), spect.size(1))
out = model(Variable(spect))
out = out.transpose(0, 1) # TxNxH
decoded_output = decoder.decode(out.data)
print(decoded_output[0])
model = DeepSpeech.load_model(args.model_path, cuda=args.cuda)
model.eval()
labels = DeepSpeech.get_labels(model)
audio_conf = DeepSpeech.get_audio_conf(model)
if args.decoder == "beam":
from decoder import BeamCTCDecoder
decoder = BeamCTCDecoder(labels, lm_path=args.lm_path, alpha=args.alpha, beta=args.beta,
cutoff_top_n=args.cutoff_top_n, cutoff_prob=args.cutoff_prob,
beam_width=args.beam_width, num_processes=args.lm_workers)
else:
decoder = GreedyDecoder(labels, blank_index=labels.index('_'))
parser = SpectrogramParser(audio_conf, normalize=True)
###--- server setup
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_address = ('', 10001)
print('starting up on %s port %s' % server_address)
sock.bind(server_address)
sock.listen(1)
file_name = 'data/recorded.wav'
bFileFound = 0
while True:
# Wait for a connection
connection, client_address = sock.accept()
print('connection from', client_address)
model = DeepSpeech.load_model(args.model_path)
device = torch.device("cuda" if args.cuda else "cpu")
model = model.to(device)
model.eval()
labels = DeepSpeech.get_labels(model)
audio_conf = DeepSpeech.get_audio_conf(model)
if args.decoder == "beam":
from decoder import BeamCTCDecoder
decoder = BeamCTCDecoder(labels, lm_path=args.lm_path, alpha=args.alpha, beta=args.beta,
cutoff_top_n=args.cutoff_top_n, cutoff_prob=args.cutoff_prob,
beam_width=args.beam_width, num_processes=args.lm_workers)
else:
decoder = GreedyDecoder(labels, blank_index=labels.index('_'))
parser = SpectrogramParser(audio_conf, cache_path=args.cache_dir,
normalize='max_frame', channel=args.channel, augment=True)
decoded_output, decoded_offsets = transcribe(args.audio_path, parser, model, decoder, device)
output = decode_results(model, decoded_output, decoded_offsets)
output['input'] = {
'channel': args.channel,
'source': args.audio_path}
output['model'] = {
'model': args.model_path,
}
print(json.dumps(output, ensure_ascii=False))