def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
window_length = 2048 # Model specific constant.
input_details, output_details = tflite_model.get_model_detail(
FLAGS.model_path)
input_wav_length = input_details[0]['shape'][0]
output_roll_length = output_details[0]['shape'][1]
assert (input_wav_length - window_length) % (output_roll_length - 1) == 0
hop_size = (input_wav_length - window_length) // (output_roll_length - 1)
overlap_timesteps = 4
overlap_wav = hop_size * overlap_timesteps + window_length
results = multiprocessing.Queue()
results_thread = threading.Thread(target=result_collector,
args=(results, ))
results_thread.start()
if FLAGS.wav_file:
model = tflite_model.Model(model_path=FLAGS.model_path)
wav_data = tf.gfile.Open(FLAGS.wav_file, 'rb').read()
samples = audio_io.wav_data_to_samples(wav_data, MODEL_SAMPLE_RATE)
samples = samples[:MODEL_SAMPLE_RATE * 10] # Only the first 10 seconds
samples = samples.reshape((-1, 1))
samples_length = samples.shape[0]
# Extend samples with zeros
samples = np.pad(samples, (0, input_wav_length), mode='constant')
for i, pos in enumerate(
range(0, samples_length - input_wav_length + overlap_wav,
input_wav_length - overlap_wav)):
chunk = samples[pos:pos + input_wav_length]
task = OnsetsTask(AudioChunk(i, chunk))
task(model)
results.put(task)
else:
tasks = multiprocessing.JoinableQueue()
## Make and start the workers
num_workers = 4
workers = [
TfLiteWorker(FLAGS.model_path, tasks, results)
for i in range(num_workers)
]
for w in workers:
w.start()
audio_feeder = AudioQueue(
callback=lambda audio_chunk: tasks.put(OnsetsTask(audio_chunk)),
audio_device_index=FLAGS.mic
if FLAGS.mic is None else int(FLAGS.mic),
sample_rate_hz=int(FLAGS.sample_rate_hz),
frame_length=input_wav_length,
overlap=overlap_wav)
audio_feeder.start()
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
results = multiprocessing.Queue()
results_thread = threading.Thread(target=result_collector,
args=(results, ))
results_thread.start()
model = tflite_model.Model(model_path=FLAGS.model_path)
overlap_timesteps = 4
overlap_wav = model.get_hop_size(
) * overlap_timesteps + model.get_window_length()
if FLAGS.wav_file:
wav_data = open(FLAGS.wav_file, 'rb').read()
samples = audio_recorder.wav_data_to_samples(wav_data,
model.get_sample_rate())
samples = samples[:model.get_sample_rate() *
10] # Only the first 10 seconds
samples = samples.reshape((-1, 1))
samples_length = samples.shape[0]
# Extend samples with zeros
samples = np.pad(samples, (0, model.get_input_wav_length()),
mode='constant')
for i, pos in enumerate(
range(
0, samples_length - model.get_input_wav_length() +
overlap_wav,
model.get_input_wav_length() - overlap_wav)):
chunk = samples[pos:pos + model.get_input_wav_length()]
task = OnsetsTask(AudioChunk(i, chunk))
task(model)
results.put(task)
else:
tasks = multiprocessing.JoinableQueue()
## Make and start the workers
num_workers = 4
workers = [
TfLiteWorker(FLAGS.model_path, tasks, results)
for i in range(num_workers)
]
for w in workers:
w.start()
audio_feeder = AudioQueue(
callback=lambda audio_chunk: tasks.put(OnsetsTask(audio_chunk)),
audio_device_index=FLAGS.mic
if FLAGS.mic is None else int(FLAGS.mic),
sample_rate_hz=int(FLAGS.sample_rate_hz),
model_sample_rate=model.get_sample_rate(),
frame_length=model.get_input_wav_length(),
overlap=overlap_wav)
audio_feeder.start()
def setup(self):
if self._model is not None:
return
self._model = tflite_model.Model(model_path=self._model_path)