def main():
parser = argparse.ArgumentParser(prog="SelfAttentionDS2 Histogram")
parser.add_argument("--config", type=str, default=None,
help="Config file")
parser.add_argument("--audio", type=str, default=None,
help="Audio file")
parser.add_argument("--saved_model", type=str, default=None,
help="Saved model")
parser.add_argument("--from_weights", type=bool, default=False,
help="Load from weights")
parser.add_argument("--output", type=str, default=None,
help="Output dir storing histograms")
args = parser.parse_args()
config = UserConfig(args.config, args.config, learning=False)
speech_featurizer = SpeechFeaturizer(config["speech_config"])
text_featurizer = TextFeaturizer(config["decoder_config"])
text_featurizer.add_scorer(Scorer(**text_featurizer.decoder_config["lm_config"],
vocabulary=text_featurizer.vocab_array))
f, c = speech_featurizer.compute_feature_dim()
satt_ds2_model = SelfAttentionDS2(input_shape=[None, f, c],
arch_config=config["model_config"],
num_classes=text_featurizer.num_classes)
satt_ds2_model._build([1, 50, f, c])
if args.from_weights:
satt_ds2_model.load_weights(args.saved_model)
else:
saved_model = tf.keras.models.load_model(args.saved_model)
satt_ds2_model.set_weights(saved_model.get_weights())
satt_ds2_model.summary(line_length=100)
satt_ds2_model.add_featurizers(speech_featurizer, text_featurizer)
signal = read_raw_audio(args.audio, speech_featurizer.sample_rate)
features = speech_featurizer.extract(signal)
decoded = satt_ds2_model.recognize_beam(tf.expand_dims(features, 0), lm=True)
print(bytes_to_string(decoded.numpy()))
for i in range(1, len(satt_ds2_model.base_model.layers)):
func = tf.keras.backend.function([satt_ds2_model.base_model.input],
[satt_ds2_model.base_model.layers[i].output])
data = func([np.expand_dims(features, 0), 1])[0][0]
print(data.shape)
data = data.flatten()
plt.hist(data, 200, color='green', histtype="stepfilled")
plt.title(f"Output of {satt_ds2_model.base_model.layers[i].name}", fontweight="bold")
plt.savefig(os.path.join(
args.output, f"{i}_{satt_ds2_model.base_model.layers[i].name}.png"))
plt.clf()
plt.cla()
plt.close()
fc = satt_ds2_model(tf.expand_dims(features, 0), training=False)
plt.hist(fc[0].numpy().flatten(), 200, color="green", histtype="stepfilled")
plt.title(f"Output of {satt_ds2_model.layers[-1].name}", fontweight="bold")
plt.savefig(os.path.join(args.output, f"{satt_ds2_model.layers[-1].name}.png"))
plt.clf()
plt.cla()
plt.close()
fc = tf.nn.softmax(fc)
plt.hist(fc[0].numpy().flatten(), 10, color="green", histtype="stepfilled")
plt.title("Output of softmax", fontweight="bold")
plt.savefig(os.path.join(args.output, "softmax_hist.png"))
plt.clf()
plt.cla()
plt.close()
plt.hist(features.flatten(), 200, color="green", histtype="stepfilled")
plt.title("Log Mel Spectrogram", fontweight="bold")
plt.savefig(os.path.join(args.output, "log_mel_spectrogram.png"))
plt.clf()
plt.cla()
plt.close()
outputs = merge_two_last_dims(inputs)
outputs = self.lstm(outputs, training=training)
return self.dense(outputs, training=training)
model = CtcModel(base_model=BaseModel(),
num_classes=text_featurizer.num_classes)
model._build(speech_featurizer.shape)
model.summary(line_length=100)
model.add_featurizers(speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer)
features = tf.random.normal(shape=[5, 50, 80, 1], dtype=tf.float32)
hyp = model.recognize(features)
print(bytes_to_string(hyp.numpy()))
hyp = model.recognize_beam(features)
print(bytes_to_string(hyp.numpy()))
hyp = model.recognize_beam(features, lm=True)
print(bytes_to_string(hyp.numpy()))
# signal = read_raw_audio("/home/nlhuy/Desktop/test/11003.wav", speech_featurizer.sample_rate)
signal = tf.random.normal(shape=[500], dtype=tf.float32)
hyp = model.recognize_tflite(signal)
print(hyp.numpy())
hyp = model.recognize_beam_tflite(signal)
print(hyp.numpy())
def main():
parser = argparse.ArgumentParser(prog="SelfAttentionDS2 Histogram")
parser.add_argument("--config", type=str, default=None, help="Config file")
parser.add_argument("--audio", type=str, default=None, help="Audio file")
parser.add_argument("--saved_model",
type=str,
default=None,
help="Saved model")
parser.add_argument("--from_weights",
type=bool,
default=False,
help="Load from weights")
parser.add_argument("--output",
type=str,
default=None,
help="Output dir storing histograms")
args = parser.parse_args()
config = UserConfig(args.config, args.config, learning=False)
speech_featurizer = SpeechFeaturizer(config["speech_config"])
text_featurizer = CharFeaturizer(config["decoder_config"])
text_featurizer.add_scorer(
Scorer(**text_featurizer.decoder_config["lm_config"],
vocabulary=text_featurizer.vocab_array))
f, c = speech_featurizer.compute_feature_dim()
satt_ds2_model = SelfAttentionDS2(input_shape=[None, f, c],
arch_config=config["model_config"],
num_classes=text_featurizer.num_classes)
satt_ds2_model._build([1, 50, f, c])
if args.from_weights:
satt_ds2_model.load_weights(args.saved_model)
else:
saved_model = tf.keras.models.load_model(args.saved_model)
satt_ds2_model.set_weights(saved_model.get_weights())
satt_ds2_model.summary(line_length=100)
satt_ds2_model.add_featurizers(speech_featurizer, text_featurizer)
signal = read_raw_audio(args.audio, speech_featurizer.sample_rate)
features = speech_featurizer.extract(signal)
decoded = satt_ds2_model.recognize_beam(tf.expand_dims(features, 0),
lm=True)
print(bytes_to_string(decoded.numpy()))
# for i in range(1, len(satt_ds2_model.base_model.layers)):
# func = tf.keras.backend.function([satt_ds2_model.base_model.input],
# [satt_ds2_model.base_model.layers[i].output])
# data = func([np.expand_dims(features, 0), 1])[0][0]
# print(data.shape)
# plt.figure(figsize=(16, 5))
# ax = plt.gca()
# im = ax.imshow(data.T, origin="lower", aspect="auto")
# ax.set_title(f"{satt_ds2_model.base_model.layers[i].name}", fontweight="bold")
# divider = make_axes_locatable(ax)
# cax = divider.append_axes("right", size="5%", pad=0.05)
# plt.colorbar(im, cax=cax)
# plt.savefig(os.path.join(
# args.output, f"{i}_{satt_ds2_model.base_model.layers[i].name}.png"))
# plt.clf()
# plt.cla()
# plt.close()
fc = satt_ds2_model(tf.expand_dims(features, 0), training=False)
plt.figure(figsize=(16, 5))
ax = plt.gca()
ax.set_title(f"{satt_ds2_model.layers[-1].name}", fontweight="bold")
im = ax.imshow(fc[0].numpy().T, origin="lower", aspect="auto")
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.savefig(
os.path.join(args.output, f"{satt_ds2_model.layers[-1].name}.png"))
plt.clf()
plt.cla()
plt.close()
fc = tf.nn.softmax(fc)
plt.figure(figsize=(16, 5))
ax = plt.gca()
ax.set_title("Softmax", fontweight="bold")
im = ax.imshow(fc[0].numpy().T, origin="lower", aspect="auto")
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.savefig(os.path.join(args.output, "softmax.png"))
plt.clf()
plt.cla()
plt.close()
plt.figure(figsize=(16, 5))
ax = plt.gca()
ax.set_title("Log Mel Spectrogram", fontweight="bold")
im = ax.imshow(features[:, :, 0].T, origin="lower", aspect="auto")
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.savefig(os.path.join(args.output, "features.png"))
plt.clf()
plt.cla()
plt.close()