Automatic Speech Recognition
Transformers
Safetensors
English
asr_model
feature-extraction
asr
speech-recognition
speech-to-text
audio
speech-llm
word-timestamps
speaker-diarization
qwen
granite-speech
custom_code
Instructions to use mazesmazes/tiny-audio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mazesmazes/tiny-audio with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mazesmazes/tiny-audio", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mazesmazes/tiny-audio", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 437 Bytes
c5a1720 470aa82 c5a1720 470aa82 c5a1720 470aa82 c5a1720 470aa82 c5a1720 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"delta_win_length": 3,
"feature_extractor_type": "GraniteSpeech5FeatureExtractor",
"feature_size": 320,
"hop_length": 160,
"logmel_floor_db": 8.0,
"n_fft": 512,
"num_mel_bins": 80,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000,
"win_length": 400,
"processor_class": "ASRProcessor",
"auto_map": {
"AutoProcessor": "asr_processing.ASRProcessor"
}
} |