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Update neural_acoustic_distance.py
Browse files- neural_acoustic_distance.py +11 -9
neural_acoustic_distance.py
CHANGED
@@ -54,6 +54,13 @@ def load_wav2vec2_featurizer(model_id: str, layer: Optional[int] = None):
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model.cuda()
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# st.success("Done!")
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@torch.no_grad()
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def _featurize(path):
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input_values, rate = sf.read(path, dtype=np.float32)
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@@ -83,16 +90,9 @@ def load_wav2vec2_featurizer(model_id: str, layer: Optional[int] = None):
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return hidden_state
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return _featurize
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#@st.cache(persist=True, show_spinner=False, max_entries=3)
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def run(model_id, layer, filename_x, filename_y):
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featurizer = load_wav2vec2_featurizer(model_id, layer)
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with st.spinner("Measuring distance..."):
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feats_x =
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feats_y =
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print('3. Features computed', datetime.now().strftime('%d-%m-%Y %H:%M:%S')) # test
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gcm = aligner(feats_x, feats_y)
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print('4. Alignments computed', datetime.now().strftime('%d-%m-%Y %H:%M:%S')) # test
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@@ -102,6 +102,8 @@ def run(model_id, layer, filename_x, filename_y):
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c, n = compute_costs(gcm)
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print('5. Costs computed', datetime.now().strftime('%d-%m-%Y %H:%M:%S')) # test
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return d, c, n
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model.cuda()
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# st.success("Done!")
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return model
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#@st.cache(persist=True, show_spinner=False, max_entries=3)
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def run(model_id, layer, filename_x, filename_y):
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model = load_wav2vec2_featurizer(model_id, layer)
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@torch.no_grad()
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def _featurize(path):
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input_values, rate = sf.read(path, dtype=np.float32)
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return hidden_state
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with st.spinner("Measuring distance..."):
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feats_x = _featurize(filename_x)
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feats_y = _featurize(filename_y)
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print('3. Features computed', datetime.now().strftime('%d-%m-%Y %H:%M:%S')) # test
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gcm = aligner(feats_x, feats_y)
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print('4. Alignments computed', datetime.now().strftime('%d-%m-%Y %H:%M:%S')) # test
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c, n = compute_costs(gcm)
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print('5. Costs computed', datetime.now().strftime('%d-%m-%Y %H:%M:%S')) # test
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del model
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return d, c, n
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