Instructions to use hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("hf-internal-testing/tiny-random-VoxtralRealtimeForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
File size: 399 Bytes
836d05f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"feature_extractor": {
"feature_extractor_type": "VoxtralRealtimeFeatureExtractor",
"feature_size": 128,
"global_log_mel_max": 1.5,
"hop_length": 160,
"n_fft": 400,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000,
"win_length": 400
},
"processor_class": "VoxtralRealtimeProcessor"
}
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