def main_deepspeech(args):
args = parse_args_deep() if args is None else args
print('Loading model from file %s' % (args.model), file=sys.stderr)
model_load_start = timer()
ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if args.lm and args.trie:
print('Loading language model from files %s %s' % (args.lm, args.trie),
file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end),
file=sys.stderr)
fs, audio = wave.read(args.audio)
# We can assume 16kHz
audio_length = len(audio) * (1 / 16000)
assert fs == 16000, "Only 16000Hz input WAV files are supported for now!"
print('Running inference.', file=sys.stderr)
inference_start = timer()
print(ds.stt(audio, fs))
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' %
(inference_end, audio_length),
file=sys.stderr)
def build_model(init_settings):
"""
:param init_settings: Configparser
:return:
"""
print('Loading DeepSpeech Models')
try:
ds = Model(str(init_settings['deepspeech']['model_path']),
int(init_settings['deepspeech']['N_FEATURES']),
int(init_settings['deepspeech']['N_CONTEXT']),
str(init_settings['deepspeech']['alphabet_path']),
int(init_settings['deepspeech']['BEAM_WIDTH']))
ds.enableDecoderWithLM(
str(init_settings['deepspeech']['alphabet_path']),
str(init_settings['deepspeech']['lm_path']),
str(init_settings['deepspeech']['trie_path']),
float(init_settings['deepspeech']['LM_WEIGHT']),
float(init_settings['deepspeech']['WORD_COUNT_WEIGHT']),
float(init_settings['deepspeech']['VALID_WORD_COUNT_WEIGHT']))
return ds
except Exception as e:
print('Loading Error!')
print(e)
return None
class DeepSpeechImp:
ds = None
def __init__(self):
logging.info('Loading model from file %s' % (shared_params.DS_MODEL))
model_load_start = timer()
self.ds = Model(shared_params.DS_MODEL, N_FEATURES, N_CONTEXT,
shared_params.DS_ALPHABET, shared_params.BEAM_WIDTH)
model_load_end = timer() - model_load_start
logging.info('Loaded model in %0.3fs.' % (model_load_end))
logging.info('Loading language model from files %s %s' %
(shared_params.DS_LANGUAGE_MODEL, shared_params.DS_TRIE))
lm_load_start = timer()
self.ds.enableDecoderWithLM(shared_params.DS_ALPHABET,
shared_params.DS_LANGUAGE_MODEL,
shared_params.DS_TRIE,
shared_params.LM_WEIGHT,
shared_params.WORD_COUNT_WEIGHT,
shared_params.VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
logging.info('Loaded language model in %0.3fs.' % (lm_load_end))
def process_audio(self, audio_path):
try:
fs, audio = wav.read(audio_path)
return self.ds.stt(audio, fs)
except Exception as ex:
logging.error(str(ex))
return ""
def __del__(self):
del self.ds
def __init__(self,
model_path,
alphabet_path,
language_model_path=None,
trie_path=None):
"""
Constructor.
:param model_path: Absolute path to (acoustic) model file.
:param alphabet_path: Absolute path to file containing alphabet.
:param language_model_path: Absolute path to language model file. This parameter is optional. Set to
enable decoding with language model.
:param trie_path: Absolute path to trie. This parameter is optional. Set to enable decoding with language model.
"""
# https://github.com/mozilla/DeepSpeech/blob/master/native_client/python/client.py
self._model = Model(aModelPath=model_path,
aNCep=26,
aNContext=9,
aAlphabetConfigPath=alphabet_path,
aBeamWidth=500)
if language_model_path is not None and trie_path is not None:
self._model.enableDecoderWithLM(aAlphabetConfigPath=alphabet_path,
aLMPath=language_model_path,
aTriePath=trie_path,
aLMWeight=1.75,
aWordCountWeight=1.0,
aValidWordCountWeight=1.0)
self._with_language_model = True
else:
self._with_language_model = False
def main():
model = "models/output_graph.pb"
alphabet = "models/alphabet.txt"
lm = "models/lm.binary"
trie = "models/trie"
ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT, WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
with open("flickr_audio_transcription.txt", "w") as out:
for audio_f in glob.glob(
"/roaming/gchrupal/vgs/data/flickr8k/flickr_audio/wavs/*.wav"):
print("Transcribing {}".format(audio_f))
try:
fs, audio = wav.read(audio_f)
if fs != 16000:
if fs < 16000:
print(
'Warning: original sample rate (%d) is lower than 16kHz. Up-sampling might produce erratic speech recognition.'
% (fs),
file=sys.stderr)
fs, audio = convert_samplerate(args.audio)
audio_length = len(audio) * (1 / 16000)
basename, ext = os.path.splitext(os.path.basename(audio_f))
out.write("{}\t{}\n".format(basename, ds.stt(audio, fs)))
out.flush()
except ValueError as e:
print("Error: {}".format(e))
def recognize(model="../models/output_graph.pb",
audio="../audio/2830-3980-0043.wav",
alphabet="../models/alphabet.txt",
lm="../models/lm.binary",
trie="../models/trie"):
print('Loading model from file %s' % (model), file=sys.stderr)
model_load_start = timer()
ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if lm and trie:
print('Loading language model from files %s %s' % (lm, trie),
file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end),
file=sys.stderr)
fs, audio = wav.read(audio)
# We can assume 16kHz
audio_length = len(audio) * (1 / 16000)
assert fs == 16000, "Only 16000Hz input WAV files are supported for now!"
print('Running inference.', file=sys.stderr)
inference_start = timer()
result = ds.stt(audio, fs)
print(result, file=sys.stderr)
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' %
(inference_end, audio_length),
file=sys.stderr)
return result
def load_model():
args = {
'model': './models/output_graph.pb',
'alphabet': './models/alphabet.txt',
'lm': './models/lm.binary',
'trie': './models/trie',
'audio': './sample_input.wav'
}
print('Loading model from file {}'.format(args['model']), file=sys.stderr)
model_load_start = timer()
ds = Model(args['model'], N_FEATURES, N_CONTEXT, args['alphabet'],
BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in {:.3}s.'.format(model_load_end), file=sys.stderr)
if args['lm'] and args['trie']:
print('Loading language model from files {} {}'.format(
args['lm'], args['trie']),
file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args['alphabet'],
args['lm'],
args['trie'],
aLMWeight=LM_WEIGHT,
aValidWordCountWeight=VALID_WORD_COUNT_WEIGHT,
aWordCountWeight=WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in {:.3}s.'.format(lm_load_end),
file=sys.stderr)
return ds
class DeepSpeech:
def __init__(self, model, alphabet, lm=None, trie=None):
print('Loading model from file %s' % (model), file=sys.stderr)
model_load_start = timer()
self.ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if lm is not None and trie is not None:
print('Loading language model from files %s %s' % (lm, trie),
file=sys.stderr)
lm_load_start = timer()
self.ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end),
file=sys.stderr)
def stt(self, audio_file):
fs, audio = wav.read(audio_file)
audio_length = len(audio) * (1 / 16000)
print('Running inference.', file=sys.stderr)
inference_start = timer()
stt_result = self.ds.stt(audio, fs)
print('Return result: ', stt_result)
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' %
(inference_end, audio_length),
file=sys.stderr)
return stt_result
def __init__(self, modelPath, alphabet, lmPath, trie, numFeatures=26, numContext=9, beamWidth=500):
print('Loading model from file %s' % modelPath, file=sys.stderr)
model_load_start = timer()
self.model = Model(modelPath, numFeatures, numContext, alphabet, beamWidth)
#self.model.enableDecoderWithLM(alphabet, lmPath, trie, LM_WEIGHT, WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
def __init__(self, vocabulary, graph="models/output_graph.pb",
alphabet="models/alphabet.txt"):
self._logger = logging.getLogger(__name__)
self._logger.debug("Initializing DeepSpeech with graph '%s' " +
"and alphabet '%s'", graph, alphabet)
self._model = Model(graph, 26, 9, alphabet, 500)
class SpeechRecognizer:
def __init__(self):
self._model = Model(MODEL_PATH, N_FEATURES, N_CONTEXT, ALPHABET_PATH,
BEAM_WIDTH)
self._model.enableDecoderWithLM(ALPHABET_PATH, LANGUAGE_MODEL_PATH,
TRIE_PATH, LM_WEIGHT,
WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
def speech_to_text(self, audio_buffer, sample_rate):
app.logger.info('processing audio file')
audio = self._process_audio_data(audio_buffer, sample_rate)
app.logger.info('starting recognition')
start = time()
text = self._model.stt(audio, SAMPLE_RATE)
end = time()
app.logger.info('finished in {:.3f}s'.format(end - start))
return text
def _process_audio_data(self, audio_buffer, original_sample_rate):
audio = np.frombuffer(audio_buffer, dtype=np.int16)
if original_sample_rate != SAMPLE_RATE:
audio = self._resample(audio, original_sample_rate)
return audio
def _resample(self, audio, original_sample_rate):
audio_length = len(audio) / original_sample_rate
samples = int(audio_length * SAMPLE_RATE)
return signal.resample(audio, samples).astype(np.int16)
def _worker_thread(self):
print('restoring from {}'.format(model_file))
model = Model(model_file, N_INPUT, N_CONTEXT, ALPHABET_CONFIG_PATH,
BEAM_WIDTH)
model.enableDecoderWithLM(ALPHABET_CONFIG_PATH, LM_BINARY_PATH,
LM_TRIE_PATH, LM_WEIGHT, WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
while True:
cmd, *args = self._queue.get()
if cmd == 'sample':
sample = args[0]
file = wave.open(sample.wav_path)
audio = np.frombuffer(file.readframes(file.getnframes()),
dtype=np.int16)
fs = file.getframerate()
start = time.time()
result = model.stt(audio, fs)
inference_time = time.time() - start
wav_time = wav_length(sample.wav_path)
print('wav length: {}\ninference time: {}\nRTF: {:2f}'.format(
wav_time, inference_time, inference_time / wav_time))
self.inference_done.emit(sample, result)
elif cmd == 'stop':
break
sess.close()
class transciber(object):
def __init__(self,
modelPath,
alphabet,
lmPath,
trie,
numFeatures=26,
numContext=9,
beamWidth=500):
print('Loading model from file %s' % modelPath, file=sys.stderr)
model_load_start = timer()
self.model = Model(modelPath, numFeatures, numContext, alphabet,
beamWidth)
self.model.enableDecoderWithLM(alphabet, lmPath, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
def transcribe(self, audioPath):
fs, audio = wav.read(audioPath)
audio_length = len(audio) * (1 / 16000)
label = self.model.stt(audio, fs)
print(label)
return label
def main():
parser = argparse.ArgumentParser(
description='Benchmarking tooling for DeepSpeech native_client.')
parser.add_argument('model',
type=str,
help='Path to the model (protocol buffer binary file)',
default="models/output_graph.pb")
parser.add_argument('audio',
type=str,
help='Path to the audio file to run (WAV format)',
default="sample_input.wav")
parser.add_argument(
'alphabet',
type=str,
help=
'Path to the configuration file specifying the alphabet used by the network',
default="models/alphabet.txt")
parser.add_argument('lm',
type=str,
nargs='?',
help='Path to the language model binary file',
default="models/lm.binary")
parser.add_argument(
'trie',
type=str,
nargs='?',
help=
'Path to the language model trie file created with native_client/generate_trie',
default="models/trie")
args = parser.parse_args()
print('Loading model from file %s' % (args.model), file=sys.stderr)
model_load_start = timer()
ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if args.lm and args.trie:
print('Loading language model from files %s %s' % (args.lm, args.trie),
file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end),
file=sys.stderr)
fs, audio = wav.read(args.audio)
# We can assume 16kHz
audio_length = len(audio) * (1 / 16000)
assert fs == 16000, "Only 16000Hz input WAV files are supported for now!"
print('Running inference.', file=sys.stderr)
inference_start = timer()
print(ds.stt(audio, fs))
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' %
(inference_end, audio_length),
file=sys.stderr)
def __init__(self):
self._model = Model(MODEL_PATH, N_FEATURES, N_CONTEXT, ALPHABET_PATH,
BEAM_WIDTH)
self._model.enableDecoderWithLM(ALPHABET_PATH, LANGUAGE_MODEL_PATH,
TRIE_PATH, LM_WEIGHT,
WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
def main(model, alphabet, lm, trie, dest):
# parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.')
# parser.add_argument('model', type=str,
# help='Path to the model (protocol buffer binary file)')
# parser.add_argument('alphabet', type=str,
# help='Path to the configuration file specifying the alphabet used by the network')
# parser.add_argument('lm', type=str, nargs='?',
# help='Path to the language model binary file')
# parser.add_argument('trie', type=str, nargs='?',
# help='Path to the language model trie file created with native_client/generate_trie')
# parser.add_argument('audio', type=str,
# help='Path to the audio file to run (WAV format)')
# args = parser.parse_args()
# print(args);
print('Loading model from file %s' % (model), file=sys.stderr)
model_load_start = timer()
ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if lm and trie:
print('Loading language model from files %s %s' % (lm, trie), file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end), file=sys.stderr)
# fs, audio = read_video(args.audio) #wav.read(args.audio)
# return ;
print('Running inference.', file=sys.stderr)
clips = os.listdir(dest) ; # clips dir path
subs = [] ;
for i, clip in enumerate(clips) :
fs, audio = wav.read(dest + str(i) + '.wav') ;
if fs != 16000:
if fs < 16000:
print('Warning: original sample rate (%d) is lower than 16kHz. Up-sampling might produce erratic speech recognition.' % (fs), file=sys.stderr)
fs, audio = convert_samplerate(dest + str(i) + '.wav')
audio_length = len(audio) * ( 1 / 16000)
inference_start = timer()
subs.append(ds.stt(audio, fs)) ;
print(subs[len(subs) - 1]);
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)
# break ;
return subs ;
def main():
parser = argparse.ArgumentParser(
description='Benchmarking tooling for DeepSpeech native_client.')
parser.add_argument('model',
help='Path to the model (protocol buffer binary file)')
parser.add_argument(
'alphabet',
help=
'Path to the configuration file specifying the alphabet used by the network'
)
parser.add_argument('lm',
nargs='?',
help='Path to the language model binary file')
parser.add_argument(
'trie',
nargs='?',
help=
'Path to the language model trie file created with native_client/generate_trie'
)
parser.add_argument('audio',
help='Path to the audio file to run (WAV format)')
args = parser.parse_args()
print('Loading model from file %s' % (args.model), file=sys.stderr)
model_load_start = timer()
ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if args.lm and args.trie:
print('Loading language model from files %s %s' % (args.lm, args.trie),
file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end),
file=sys.stderr)
fs, audio = wav.read(args.audio)
if fs != 16000:
if fs < 16000:
print(
'Warning: original sample rate (%d) is lower than 16kHz. Up-sampling might produce erratic speech recognition.'
% (fs),
file=sys.stderr)
fs, audio = convert_samplerate(args.audio)
audio_length = len(audio) * (1 / 16000)
print('Running inference.', file=sys.stderr)
inference_start = timer()
print(ds.stt(audio, fs))
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' %
(inference_end, audio_length),
file=sys.stderr)
def setup_model(model_path, alphabet, lm, trie):
if model_path and alphabet:
print("creating model {} {}".format(model_path, alphabet))
ds_model = Model(model_path, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
if lm and trie:
ds_model.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
return ds_model
return None
def __init__(self, *args, **kwargs):
plugin.STTPlugin.__init__(self, *args, **kwargs)
self._logger = logging.getLogger(__name__)
self._plugin_config = self.profile['deepspeech']
graph = self._plugin_config['graph']
alphabet = self._plugin_config['alphabet']
self._logger.debug(
"Initializing DeepSpeech with graph '%s' " + "and alphabet '%s'",
graph, alphabet)
self._model = Model(graph, 26, 9, alphabet, 500)
def load_model():
model_path = 'output_graph.pb'
alphabet_path = 'alphabet.txt'
lm_path = 'lm.binary'
trie_path = 'trie'
ds = Model(model_path, N_FEATURES, N_CONTEXT, alphabet_path, BEAM_WIDTH)
ds.enableDecoderWithLM(alphabet_path, lm_path, trie_path, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
return ds
def setup_model(model_path, alphabet, lm, trie):
log("creating model {} {}...".format(model_path, alphabet))
ds_model = Model(model_path, N_FEATURES, N_CONTEXT, alphabet,
BEAM_WIDTH)
if lm and trie:
ds_model.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
log("model is ready.")
return ds_model
def main():
parser = argparse.ArgumentParser(description='Running DeepSpeech inference.')
parser.add_argument('--model',
help='Path to the model (protocol buffer binary file)')
parser.add_argument('--alphabet',
help='Path to the configuration file specifying the alphabet used by the network')
parser.add_argument('--lm', nargs='?',
help='Path to the language model binary file')
parser.add_argument('--trie', nargs='?',
help='Path to the language model trie file created with native_client/generate_trie')
parser.add_argument('--audio',
help='Path to the audio file to run (WAV format)')
parser.add_argument('--version',
help='Print version and exits')
args = parser.parse_args()
if args.version:
print_versions()
return 0
print('Loading model from file {}'.format(args.model), file=sys.stderr)
model_load_start = timer()
ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in {:.3}s.'.format(model_load_end), file=sys.stderr)
if args.lm and args.trie:
print('Loading language model from files {} {}'.format(args.lm, args.trie), file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in {:.3}s.'.format(lm_load_end), file=sys.stderr)
fin = wave.open(args.audio, 'rb')
fs = fin.getframerate()
if fs != 16000:
print('Warning: original sample rate ({}) is different than 16kHz. Resampling might produce erratic speech recognition.'.format(fs), file=sys.stderr)
fs, audio = convert_samplerate(args.audio)
else:
audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
audio_length = fin.getnframes() * (1/16000)
fin.close()
print('Running inference.', file=sys.stderr)
inference_start = timer()
print(ds.stt(audio, fs))
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)
def load_model(self):
start = timer()
self.model = Model(self.graph, self.n_features, self.n_context, self.alphabet, self.beam_width)
end = timer()
print('Loaded model in %0.3fs.' % (end - start))
if self.lm is not None and self.trie is not None:
start = timer()
self.model.enableDecoderWithLM(
self.alphabet, self.lm,
self.trie, self.lm_weight,
self.word_count_weight,
self.valid_word_count_weight
)
end = timer()
print('Loaded language model in %0.3fs.' % (end - start))
def loadModel():
global ds
print('Loading model from file %s' % (MODEL_FILE), file=sys.stderr)
model_load_start = timer()
ds = Model(MODEL_FILE, N_FEATURES, N_CONTEXT, ALPHABET_FILE, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
print('Loading language model from files %s %s' %
(LM_BINARY_FILE, TRIE_FILE),
file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(ALPHABET_FILE, LM_BINARY_FILE, TRIE_FILE, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end), file=sys.stderr)
def stt(audioPath):
model = conf.get_config('model')
alphabet = conf.get_config('alphabet')
lm = conf.get_config('lm')
trie = conf.get_config('trie')
ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
if lm and trie:
ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
fs, audio = wav.read(audioPath)
text = ds.stt(audio, fs)
return text
def load_model(self):
print('Loading model from file %s' % (MODEL_PATH))
model_load_start = timer()
self.ds = Model(MODEL_PATH, N_FEATURES, N_CONTEXT, ALPHABET_PATH,
BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end))
print('Loading language model from files %s %s' % (LM_PATH, TRIE_PATH))
lm_load_start = timer()
self.ds.enableDecoderWithLM(ALPHABET_PATH, LM_PATH, TRIE_PATH,
LM_WEIGHT, WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end))
class DeepSpeechSTT(plugin.STTPlugin):
"""
DeepSpeech Speech-to-Text implementation
"""
def __init__(self, *args, **kwargs):
plugin.STTPlugin.__init__(self, *args, **kwargs)
self._logger = logging.getLogger(__name__)
self._plugin_config = self.profile['deepspeech']
graph = self._plugin_config['graph']
alphabet = self._plugin_config['alphabet']
self._logger.debug(
"Initializing DeepSpeech with graph '%s' " + "and alphabet '%s'",
graph, alphabet)
self._model = Model(graph, 26, 9, alphabet, 500)
def transcribe(self, fp):
"""
Performs STT, transcribing an audio file and returning the result.
Arguments:
fp -- a file object containing audio data
"""
fs, audio = wav.read(fp)
return self._model.stt(audio, fs)
def main():
parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.')
parser.add_argument('model', type=str,
help='Path to the model (protocol buffer binary file)')
parser.add_argument('audio', type=str,
help='Path to the audio file to run (WAV format)')
parser.add_argument('alphabet', type=str,
help='Path to the configuration file specifying the alphabet used by the network')
parser.add_argument('lm', type=str, nargs='?',
help='Path to the language model binary file')
parser.add_argument('trie', type=str, nargs='?',
help='Path to the language model trie file created with native_client/generate_trie')
args = parser.parse_args()
print('Loading model from file %s' % (args.model), file=sys.stderr)
model_load_start = timer()
ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if args.lm and args.trie:
print('Loading language model from files %s %s' % (args.lm, args.trie), file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end), file=sys.stderr)
for path in sorted(glob.glob(args.audio))[::1]:
target = os.path.splitext(path)[0] + '.txt'
if os.path.exists(target):
continue
fs, audio = wav.read(path)
# We can assume 16kHz
audio_length = len(audio) * (1 / 16000)
assert fs == 16000, "Only 16000Hz input WAV files are supported for now!"
print('Running inference of %s.' % path, file=sys.stderr)
inference_start = timer()
text = ds.stt(audio, fs)
print(text)
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)
with open(target, 'w') as out:
out.write(text)
def main():
print('Loading model from file %s' % MODEL, file=sys.stderr)
model_load_start = timer()
ds = Model(MODEL, N_FEATURES, N_CONTEXT, ALPHABET, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % model_load_end, file=sys.stderr)
# Uncomment if you want to use a language model
# =============================================
# print('Loading language model from files %s %s' % (LANGUAGE_MODEL, TRIE), file=sys.stderr)
# lm_load_start = timer()
# ds.enableDecoderWithLM(ALPHABET, LANGUAGE_MODEL, TRIE, LM_WEIGHT,
# WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
# lm_load_end = timer() - lm_load_start
# print('Loaded language model in %0.3fs.' % lm_load_end, file=sys.stderr)
# audio file
path_to_audio = 'data/sesq316qna.mp3'
# change rate of audio file to 16kHz
call = AudioSegment.from_file(path_to_audio)
call = call.set_frame_rate(16000)
# only analyze the first 2 minutes (2 * 60 * 1000)
segment = call[:120000]
# declare the new name of the audio file
path = 'data/testing.wav'
# export the audio file to wav format
segment.export(path, format="wav")
# read the new file again with the wav reader
fs, audio = wav.read(path)
# We can assume 16kHz
audio_length = len(audio) * (1 / 16000)
assert fs == 16000, "Only 16000Hz input WAV files are supported for now!"
print('Running inference.', file=sys.stderr)
inference_start = timer()
prediction_text = ds.stt(audio, fs)
print(prediction_text)
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' %
(inference_end, audio_length),
file=sys.stderr)
def __init__(self, model, alphabet, lm=None, trie=None):
print('Loading model from file %s' % (model), file=sys.stderr)
model_load_start = timer()
self.ds = Model(model, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if lm is not None and trie is not None:
print('Loading language model from files %s %s' % (lm, trie),
file=sys.stderr)
lm_load_start = timer()
self.ds.enableDecoderWithLM(alphabet, lm, trie, LM_WEIGHT,
WORD_COUNT_WEIGHT,
VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end),
file=sys.stderr)
class MozillaDeepSpeechASREngine(ASREngine):
"""https://github.com/mozilla/DeepSpeech"""
def __init__(self,
model_path,
alphabet_path,
language_model_path=None,
trie_path=None):
"""
Constructor.
:param model_path: Absolute path to (acoustic) model file.
:param alphabet_path: Absolute path to file containing alphabet.
:param language_model_path: Absolute path to language model file. This parameter is optional. Set to
enable decoding with language model.
:param trie_path: Absolute path to trie. This parameter is optional. Set to enable decoding with language model.
"""
# https://github.com/mozilla/DeepSpeech/blob/master/native_client/python/client.py
self._model = Model(aModelPath=model_path,
aNCep=26,
aNContext=9,
aAlphabetConfigPath=alphabet_path,
aBeamWidth=500)
if language_model_path is not None and trie_path is not None:
self._model.enableDecoderWithLM(aAlphabetConfigPath=alphabet_path,
aLMPath=language_model_path,
aTriePath=trie_path,
aLMWeight=1.75,
aWordCountWeight=1.0,
aValidWordCountWeight=1.0)
self._with_language_model = True
else:
self._with_language_model = False
def transcribe(self, path):
pcm, sample_rate = soundfile.read(path)
pcm = (np.iinfo(np.int16).max * pcm).astype(np.int16)
return self._model.stt(pcm, aSampleRate=sample_rate)
def __str__(self):
if self._with_language_model:
return 'Mozilla DeepSpeech (with language model)'
else:
return 'Mozilla DeepSpeech'
def main():
parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.')
parser.add_argument('model', type=str,
help='Path to the model (protocol buffer binary file)')
parser.add_argument('alphabet', type=str,
help='Path to the configuration file specifying the alphabet used by the network')
parser.add_argument('lm', type=str, nargs='?',
help='Path to the language model binary file')
parser.add_argument('trie', type=str, nargs='?',
help='Path to the language model trie file created with native_client/generate_trie')
parser.add_argument('audio', type=str,
help='Path to the audio file to run (WAV format)')
args = parser.parse_args()
print('Loading model from file %s' % (args.model), file=sys.stderr)
model_load_start = timer()
ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
model_load_end = timer() - model_load_start
print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
if args.lm and args.trie:
print('Loading language model from files %s %s' % (args.lm, args.trie), file=sys.stderr)
lm_load_start = timer()
ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
lm_load_end = timer() - lm_load_start
print('Loaded language model in %0.3fs.' % (lm_load_end), file=sys.stderr)
fs, audio = wav.read(args.audio)
if fs != 16000:
if fs < 16000:
print('Warning: original sample rate (%d) is lower than 16kHz. Up-sampling might produce erratic speech recognition.' % (fs), file=sys.stderr)
fs, audio = convert_samplerate(args.audio)
audio_length = len(audio) * ( 1 / 16000)
print('Running inference.', file=sys.stderr)
inference_start = timer()
print(ds.stt(audio, fs))
inference_end = timer() - inference_start
print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)