Instructions to use hf-tiny-model-private/tiny-random-MCTCTForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MCTCTForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-MCTCTForCTC")# Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-MCTCTForCTC", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "feature_extractor_type": "MCTCTFeatureExtractor", | |
| "feature_size": 80, | |
| "frame_signal_scale": 32768.0, | |
| "hop_length": 10, | |
| "mel_floor": 1.0, | |
| "n_fft": 512, | |
| "n_freqs": 257, | |
| "normalize_means": true, | |
| "normalize_vars": true, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "preemphasis_coeff": 0.97, | |
| "return_attention_mask": false, | |
| "sample_size": 400, | |
| "sample_stride": 160, | |
| "sampling_rate": 16000, | |
| "win_function": "hamming_window", | |
| "win_length": 25 | |
| } | |