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
File size: 502 Bytes
7605a60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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
}
|