Automatic Speech Recognition
Transformers
Safetensors
Tibetan
whisper
audio
speech-to-text
tibetan
translation
low-resource
Eval Results (legacy)
Instructions to use milanakdj/whisper-small-full-tibetan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use milanakdj/whisper-small-full-tibetan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="milanakdj/whisper-small-full-tibetan")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan") model = AutoModelForSpeechSeq2Seq.from_pretrained("milanakdj/whisper-small-full-tibetan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 356 Bytes
511e4e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"chunk_length": 30,
"dither": 0.0,
"feature_extractor_type": "WhisperFeatureExtractor",
"feature_size": 80,
"hop_length": 160,
"n_fft": 400,
"n_samples": 480000,
"nb_max_frames": 3000,
"padding_side": "right",
"padding_value": 0.0,
"processor_class": "WhisperProcessor",
"return_attention_mask": false,
"sampling_rate": 16000
}
|