def visualize_waveform() -> None:
"""MAKEDOC: what is visualize_waveform doing?"""
logg = logging.getLogger(f"c.{__name__}.visualize_waveform")
# logg.setLevel("INFO")
logg.debug("Start visualize_waveform")
dataset_path = Path("data_raw")
logg.debug(f"dataset_path: {dataset_path}")
# words = words_types["num"]
# words = words_types["all"]
words = words_types["num_loud"]
for word in words:
word_folder = dataset_path / word
sample_path = list(word_folder.iterdir())[0]
sample_sig, sr = librosa.load(sample_path, sr=None)
# logg.debug(f"sample_sig.shape: {sample_sig.shape}")
fig, ax = plt.subplots(2, 1, figsize=(10, 12))
plot_waveform(sample_sig, ax[0], title=f"Waveform for {word}")
plot_waveform(sample_sig ** 2, ax[1], title=f"Waveform**2 for {word}")
fig.tight_layout()
def evaluate_audio_cnn(args):
"""MAKEDOC: what is evaluate_audio_cnn doing?"""
logg = logging.getLogger(f"c.{__name__}.evaluate_audio_cnn")
logg.debug("Start evaluate_audio_cnn")
# magic to fix the GPUs
setup_gpus()
# need to know on which dataset the model was trained to compute specs
dataset_name = "mel01"
# words that the dataset was trained on
train_words_type = args.train_words_type
train_words = words_types[train_words_type]
# permutation from sorted to your wor(l)d order
perm_pred = compute_permutation(train_words)
rec_words_type = args.rec_words_type
if rec_words_type == "train":
rec_words = train_words
else:
rec_words = words_types[rec_words_type]
num_rec_words = len(rec_words)
# where to save the audios
audio_folder = Path("recorded_audio")
if not audio_folder.exists():
audio_folder.mkdir(parents=True, exist_ok=True)
# record the audios and save them in audio_folder
audio_path_fmt = "{}_02.wav"
audios = record_audios(rec_words, audio_folder, audio_path_fmt, timeout=0)
# compute the spectrograms and build the dataset of correct shape
img_specs = []
spec_dict = get_spec_dict()
spec_kwargs = spec_dict[dataset_name]
p2d_kwargs = {"ref": np.max}
for word in rec_words:
# get the name
audio_path = audio_folder / audio_path_fmt.format(word)
# convert it to mel
log_spec = wav2mel(audio_path, spec_kwargs, p2d_kwargs)
img_spec = log_spec.reshape((*log_spec.shape, 1))
logg.debug(f"img_spec.shape: {img_spec.shape}"
) # img_spec.shape: (128, 32, 1)
img_specs.append(img_spec)
# the data needs to look like this data['testing'].shape: (735, 128, 32, 1)
# data = log_spec.reshape((1, *log_spec.shape, 1))
data = np.stack(img_specs)
logg.debug(f"data.shape: {data.shape}")
hypa: ty.Dict[str, ty.Union[str, int]] = {}
hypa["base_dense_width"] = 32
hypa["base_filters"] = 20
hypa["batch_size"] = 32
hypa["dropout_type"] = "01"
hypa["epoch_num"] = 16
hypa["kernel_size_type"] = "02"
hypa["pool_size_type"] = "02"
hypa["learning_rate_type"] = "02"
hypa["optimizer_type"] = "a1"
hypa["dataset"] = dataset_name
hypa["words"] = train_words_type
# get the words
train_words = words_types[train_words_type]
model_name = build_cnn_name(hypa)
logg.debug(f"model_name: {model_name}")
# model_folder = Path("trained_models") / "cnn"
model_folder = Path("saved_models")
model_path = model_folder / f"{model_name}.h5"
if not model_path.exists():
logg.error(f"Model not found at: {model_path}")
raise FileNotFoundError
model = tf.keras.models.load_model(model_path)
model.summary()
pred = model.predict(data)
# logg.debug(f"pred: {pred}")
# plot the thing
plot_size = 5
fw = plot_size * 3
fh = plot_size * num_rec_words
fig, axes = plt.subplots(nrows=num_rec_words, ncols=3, figsize=(fw, fh))
fig.suptitle("Recorded audios", fontsize=18)
for i, word in enumerate(rec_words):
plot_waveform(audios[i], axes[i][0])
spec = img_specs[i][:, :, 0]
plot_spec(spec, axes[i][1])
plot_pred(
pred[i][perm_pred],
train_words,
axes[i][2],
f"Prediction for {rec_words[i]}",
train_words.index(word),
)
# https://stackoverflow.com/q/8248467
# https://stackoverflow.com/q/2418125
fig.tight_layout(h_pad=3, rect=[0, 0.03, 1, 0.97])
fig_name = f"{model_name}_{train_words_type}_{rec_words_type}.png"
results_path = audio_folder / fig_name
fig.savefig(results_path)
if num_rec_words <= 6:
plt.show()
def evaluate_attention_weights(train_words_type: str,
rec_words_type: str,
do_new_record: bool = False) -> None:
"""MAKEDOC: what is evaluate_attention_weights doing?"""
logg = logging.getLogger(f"c.{__name__}.evaluate_attention_weights")
# logg.setLevel("INFO")
logg.debug("Start evaluate_attention_weights")
# magic to fix the GPUs
setup_gpus()
# ATT_ct02_dr02_ks02_lu01_as01_qt01_dw01_opa1_lr01_bs01_en01_dsmel04_wk1
# hypa: ty.Dict[str, str] = {}
# hypa["conv_size_type"] = "02"
# hypa["dropout_type"] = "02"
# hypa["kernel_size_type"] = "02"
# hypa["lstm_units_type"] = "01"
# hypa["query_style_type"] = "01"
# hypa["dense_width_type"] = "01"
# hypa["optimizer_type"] = "a1"
# hypa["learning_rate_type"] = "01"
# hypa["batch_size_type"] = "01"
# hypa["epoch_num_type"] = "01"
# dataset_name = "mel04"
# hypa["dataset_name"] = dataset_name
# hypa["words_type"] = train_words_type
# use_validation = True
# ATT_ct02_dr01_ks01_lu01_qt05_dw01_opa1_lr03_bs02_en02_dsaug07_wLTnum
hypa = {
"batch_size_type": "02",
"conv_size_type": "02",
"dataset_name": "aug07",
"dense_width_type": "01",
"dropout_type": "01",
"epoch_num_type": "02",
"kernel_size_type": "01",
"learning_rate_type": "03",
"lstm_units_type": "01",
"optimizer_type": "a1",
"query_style_type": "05",
"words_type": "LTnum",
}
use_validation = True
dataset_name = hypa["dataset_name"]
model_name = build_attention_name(hypa, use_validation)
logg.debug(f"model_name: {model_name}")
# load the model
model_folder = Path("trained_models") / "attention"
model_path = model_folder / f"{model_name}.h5"
# model = tf.keras.models.load_model(model_path)
# https://github.com/keras-team/keras/issues/5088#issuecomment-401498334
model = tf.keras.models.load_model(
model_path, custom_objects={"backend": tf.keras.backend})
model.summary()
logg.debug(f"ascii_model(model): {ascii_model(model)}")
att_weight_model = tf.keras.models.Model(
inputs=model.input,
outputs=[
model.get_layer("output").output,
model.get_layer("att_softmax").output,
model.get_layer("bidirectional_1").output,
],
)
att_weight_model.summary()
# logg.debug(f"att_weight_model.outputs: {att_weight_model.outputs}")
# get the training words
train_words = words_types[train_words_type]
# logg.debug(f"train_words: {train_words}")
perm_pred = compute_permutation(train_words)
rec_words_type = args.rec_words_type
if rec_words_type == "train":
rec_words = train_words[-3:]
# rec_words = train_words[:]
logg.debug(f"Using rec_words: {rec_words}")
else:
rec_words = words_types[rec_words_type]
num_rec_words = len(rec_words)
# record new audios
if do_new_record:
# where to save the audios
audio_folder = Path("recorded_audio")
if not audio_folder.exists():
audio_folder.mkdir(parents=True, exist_ok=True)
# record the audios and save them in audio_folder
audio_path_fmt = "{}_02.wav"
audios = record_audios(rec_words,
audio_folder,
audio_path_fmt,
timeout=0)
# compute the spectrograms and build the dataset of correct shape
img_specs = []
spec_dict = get_spec_dict()
spec_kwargs = spec_dict[dataset_name]
p2d_kwargs = {"ref": np.max}
for word in rec_words:
# get the name
audio_path = audio_folder / audio_path_fmt.format(word)
# convert it to mel
log_spec = wav2mel(audio_path, spec_kwargs, p2d_kwargs)
img_spec = log_spec.reshape((*log_spec.shape, 1))
# logg.debug(f"img_spec.shape: {img_spec.shape}")
# img_spec.shape: (128, 32, 1)
img_specs.append(img_spec)
# the data needs to look like this data['testing'].shape: (735, 128, 32, 1)
rec_data = np.stack(img_specs)
# logg.debug(f"rec_data.shape: {rec_data.shape}")
# load data if you do not want to record new audios
else:
# input data
processed_folder = Path("data_proc")
processed_path = processed_folder / f"{dataset_name}"
# which word in the dataset to plot
word_id = 2
# the loaded spectrograms
rec_data_l: ty.List[np.ndarray] = []
for i, word in enumerate(rec_words):
data, labels = load_processed(processed_path, [word])
# get one of the spectrograms
word_data = data["testing"][word_id]
rec_data_l.append(word_data)
# turn the list into np array
rec_data = np.stack(rec_data_l)
# get prediction and attention weights
pred, att_weights, LSTM_out = att_weight_model.predict(rec_data)
# logg.debug(f"att_weights.shape: {att_weights.shape}")
# logg.debug(f"att_weights[0].shape: {att_weights[0].shape}")
# if we recorded fresh audios we also have the waveform to plot
ax_add = 1 if do_new_record else 0
# plot the wave, spectrogram, weights and predictions in each column
plot_size = 5
fw = plot_size * num_rec_words
nrows = 3 + ax_add
# nrows = 4 + ax_add
fh = plot_size * nrows * 0.7
fig, axes = plt.subplots(nrows=nrows,
ncols=num_rec_words,
figsize=(fw, fh),
sharey="row")
fig.suptitle(f"Attention weights and predictions for {rec_words}",
fontsize=20)
for i, word in enumerate(rec_words):
word_spec = rec_data[i][:, :, 0]
# logg.debug(f"word_spec.shape: {word_spec.shape}")
# plot the waveform
if do_new_record:
plot_waveform(audios[i], axes[0][i])
# plot the spectrogram
title = f"Spectrogram for {word}"
plot_spec(word_spec, axes[0 + ax_add][i], title=title)
# plot the weights
word_att_weights = att_weights[i]
# plot_att_weights(word_att_weights, axes[1 + ax_add][i], title)
word_att_weights_img = np.expand_dims(word_att_weights, axis=-1).T
axes[1 + ax_add][i].imshow(word_att_weights_img,
origin="lower",
aspect="auto")
title = f"Attention weights for {word}"
axes[1 + ax_add][i].set_title(title)
# plot the predictions
word_pred = pred[i]
# permute the prediction from sorted to the order you have
word_pred = word_pred[perm_pred]
pred_index = np.argmax(word_pred)
title = f"Predictions for {word}"
plot_pred(word_pred, train_words, axes[2 + ax_add][i], title,
pred_index)
# axes[3 + ax_add][i].imshow(LSTM_out[i], origin="lower")
# fig.tight_layout()
fig.tight_layout(h_pad=3, rect=[0, 0.03, 1, 0.97])
fig_name = f"{model_name}"
fig_name += f"_{train_words_type}"
fig_name += f"_{rec_words_type}_img"
if do_new_record:
fig_name += "_new.{}"
else:
fig_name += "_data.{}"
plot_folder = Path("plot_results")
results_path = plot_folder / fig_name.format("png")
fig.savefig(results_path)
results_path = plot_folder / fig_name.format("pdf")
fig.savefig(results_path)
if num_rec_words <= 6:
plt.show()
def visualize_spec():
"""MAKEDOC: what is visualize_spec doing?"""
logg = logging.getLogger(f"c.{__name__}.visualize_spec")
logg.debug("Start visualize_spec")
plot_folder = Path("plot_models")
dataset_path = Path("data_raw")
logg.debug(f"dataset_path: {dataset_path}")
# word = "happy"
# word = "wow"
# word = "six"
# word = "eight" # 3
word = "loudest_eight"
word_folder = dataset_path / word
# sample_path = word_folder / "0a2b400e_nohash_0.wav"
sample_path = list(word_folder.iterdir())[0]
# sample_path = "/home/pmn/free_spoken_digit_dataset/recordings/3_theo_10.wav"
# sample_path = "/home/pmn/uni/human_data/progetto2020/src/data_fsdd_raw/five/5_yweweler_30.wav"
# sample_path = "/home/pmn/uni/human_data/progetto2020/src/data_fsdd_raw/fsdd_five/5_yweweler_30.wav"
# sample_path = "/home/pmn/uni/human_data/progetto2020/src/data_fsdd_raw/fsdd_five/5_yweweler_33.wav"
# sample_path = "/home/pmn/uni/human_data/progetto2020/src/data_raw/_other/ljs_LJ001-0018_005.wav"
logg.debug(f"sample_path: {sample_path}")
# fig, ax = plt.subplots(3, 1, figsize=(12, 12))
fig, ax = plt.subplots(4, 1, figsize=(10, 15))
sample_sig, sr = librosa.load(sample_path, sr=None)
logg.debug(f"sample_sig.shape: {sample_sig.shape}")
plot_waveform(sample_sig, ax[0], sample_rate=sr, title=f"Waveform for {word}")
plot_waveform(
sample_sig ** 2, ax[1], sample_rate=sr, title=f"Waveform**2 for {word}"
)
sample_melspec = librosa.feature.melspectrogram(sample_sig, sr=sr)
logg.debug(f"sample_melspec.shape: {sample_melspec.shape}")
sample_log_melspec = librosa.power_to_db(sample_melspec, ref=np.max)
logg.debug(f"sample_log_melspec.shape: {sample_log_melspec.shape}")
plot_spec(sample_log_melspec, ax[2], title=f"Mel spectrogram for {word}")
sample_mfcc = librosa.feature.mfcc(sample_sig, sr=sr)
logg.debug(f"sample_mfcc.shape: {sample_mfcc.shape}")
sample_log_mfcc = librosa.power_to_db(sample_mfcc, ref=np.max)
logg.debug(f"sample_log_mfcc.shape: {sample_log_mfcc.shape}")
plot_spec(sample_log_mfcc, ax[3], title=f"MFCCs for {word}")
fig.tight_layout()
fig.savefig(plot_folder / f"{word}_specs.pdf")
sr = 16000
n_fft = 2048
hop_length = 512
mel_10 = librosa.filters.mel(sr=sr, n_fft=n_fft, n_mels=10)
# fig, ax = plt.subplots(1, 1, figsize=(12, 12))
fig, ax = plt.subplots(1, 1)
librosa.display.specshow(
mel_10,
sr=sr,
hop_length=hop_length,
x_axis="linear",
y_axis="linear",
cmap="viridis",
ax=ax,
)
ax.set_ylabel("Mel filter")
ax.set_xlabel("Hz")
fig.tight_layout()
fig.savefig(plot_folder / "mel10_bins.pdf")
fig, ax = plt.subplots(1, 1, figsize=(8, 4))
for i, m in enumerate(mel_10):
ax.plot(m, label=i)
ax.legend()
fig.tight_layout()
fig.savefig(plot_folder / "mel10_filterbank.pdf")
def evaluate_audio_transfer(train_words_type: str, rec_words_type: str) -> None:
"""MAKEDOC: what is evaluate_audio_transfer doing?"""
logg = logging.getLogger(f"c.{__name__}.evaluate_audio_transfer")
# logg.setLevel("INFO")
logg.debug("Start evaluate_audio_transfer")
# magic to fix the GPUs
setup_gpus()
datasets_type = "01"
datasets_types = {
"01": ["mel05", "mel09", "mel10"],
"02": ["mel05", "mel10", "mfcc07"],
"03": ["mfcc06", "mfcc07", "mfcc08"],
"04": ["mel05", "mfcc06", "melc1"],
"05": ["melc1", "melc2", "melc4"],
}
dataset_names = datasets_types[datasets_type]
# we do not support composed datasets for now
for dn in dataset_names:
if dn.startswith("melc"):
logg.error(f"not supported: {dataset_names}")
return
# words that the dataset was trained on
train_words_type = args.train_words_type
train_words = words_types[train_words_type]
# the model predicts sorted words
perm_pred = compute_permutation(train_words)
if rec_words_type == "train":
rec_words = train_words
else:
rec_words = words_types[rec_words_type]
num_rec_words = len(rec_words)
# where to save the audios
audio_folder = Path("recorded_audio")
if not audio_folder.exists():
audio_folder.mkdir(parents=True, exist_ok=True)
# record the audios and save them in audio_folder
audio_path_fmt = "{}_02.wav"
audios = record_audios(rec_words, audio_folder, audio_path_fmt, timeout=0)
# compute the spectrograms and build the dataset of correct shape
specs_3ch: ty.List[np.ndarray] = []
# params for the mel conversion
p2d_kwargs = {"ref": np.max}
spec_dict = get_spec_dict()
for word in rec_words:
# get the name
audio_path = audio_folder / audio_path_fmt.format(word)
# convert it to mel for each type of dataset
specs: ty.List[np.ndarray] = []
for dataset_name in dataset_names:
spec_kwargs = spec_dict[dataset_name]
log_spec = wav2mel(audio_path, spec_kwargs, p2d_kwargs)
specs.append(log_spec)
img_spec = np.stack(specs, axis=2)
# logg.debug(f"img_spec.shape: {img_spec.shape}") # (128, 128, 3)
specs_3ch.append(img_spec)
data = np.stack(specs_3ch)
logg.debug(f"data.shape: {data.shape}")
hypa: ty.Dict[str, str] = {}
hypa["dense_width_type"] = "03"
hypa["dropout_type"] = "01"
hypa["batch_size_type"] = "02"
hypa["epoch_num_type"] = "01"
hypa["learning_rate_type"] = "01"
hypa["optimizer_type"] = "a1"
hypa["datasets_type"] = datasets_type
hypa["words_type"] = train_words_type
use_validation = False
# hypa: Dict[str, str] = {}
# hypa["dense_width_type"] = "02"
# hypa["dropout_type"] = "01"
# hypa["batch_size_type"] = "01"
# hypa["epoch_num_type"] = "01"
# hypa["learning_rate_type"] = "01"
# hypa["optimizer_type"] = "a1"
# hypa["datasets_type"] = datasets_type
# hypa["words_type"] = train_words_type
# use_validation = True
# get the model name
model_name = build_transfer_name(hypa, use_validation)
# load the model
# model_folder = Path("trained_models") / "transfer"
model_folder = Path("saved_models")
model_path = model_folder / f"{model_name}.h5"
model = tf.keras.models.load_model(model_path)
# predict!
pred = model.predict(data)
# plot everything
plot_size = 5
fw = plot_size * 5
fh = plot_size * num_rec_words
fig, axes = plt.subplots(nrows=num_rec_words, ncols=5, figsize=(fw, fh))
fig.suptitle("Recorded audios", fontsize=18)
for i, word in enumerate(rec_words):
plot_waveform(audios[i], axes[i][0])
img_spec = specs_3ch[i]
plot_spec(img_spec[:, :, 0], axes[i][1])
plot_spec(img_spec[:, :, 1], axes[i][2])
plot_spec(img_spec[:, :, 2], axes[i][3])
plot_pred(
pred[i][perm_pred],
train_words,
axes[i][4],
f"Prediction for {rec_words[i]}",
train_words.index(word),
)
# https://stackoverflow.com/q/8248467
# https://stackoverflow.com/q/2418125
fig.tight_layout(h_pad=3, rect=[0, 0.03, 1, 0.97])
fig_name = f"{model_name}_{train_words_type}_{rec_words_type}.png"
results_path = audio_folder / fig_name
fig.savefig(results_path)
if num_rec_words <= 6:
plt.show()