import tqdm from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, CHUNK_LENGTH, pad_or_trim, log_mel_spectrogram, load_audio from .alignment import get_trellis, backtrack, merge_repeats, merge_words from .alignment import load_align_model, align, get_trellis, backtrack, merge_repeats, merg...
(X_trainFinal), img_width, img_height, img_channels), dtype=np.uint8) for n, path1 in tqdm(enumerate(X_trainFinal), total=len(X_trainFinal)): path = path1 img = imageio.imread(path)[:,:,:img_channels] img = resize(img, (img_height, img_width), mode='constant', preserve_...
while pending: p.join(interval) for t in pending[:]: if t.is_file(): # Could print(f"Done: {t}") pending.remove(t) progress.update(task, advance=1) if p.exitcode is not None: # Should be finished, but was it success or failure? pending = [] # to break the loop p.join...
[2598/2598] WARNING:root:TPU has started up successfully with version pytorch-1.11 * CUDA: - GPU: - available: False - version: 10.2 * Packages: - numpy: 1.21.6 - pyTorch_debug: False - pyTorch_version: 1.11.0+cu102 - pytorch-lightning: 1.6.5 - tqdm: 4.64.0 * System: - OS: ...
Added inline copy of tqdm to make sure it's available for progress bar output for 2.7 or higher. Recommend having it in the Debian package. Added inline copy of colorama for use on Windows, where on some terminals it will give better results with the progress bar. Stop using old PyLint ...
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for f in tqdm(funcom_q90_test[:]): fid = f['fid'] code = f['code'] code = tokenizer(code, max_length = 1024, return_tensors="pt",truncation=True).input_ids.to(device) output = model.generate(code, max_length=50) output = tokenizer.decode(output[0], skip_special_tokens=True...
["answers"]} for s_idx in range(len(dataset)) ] predicted_answers = [] for s_idx in tqdm(range(len(dataset))): example_id = dataset[s_idx]["id"] context = dataset[s_idx]["context"] answers = [] # Loop through all features associated with that example for feature_index in ...