Automatic Speech Recognition
Transformers
Safetensors
Assyrian Neo-Aramaic
wav2vec2
ctc
mms
assyrian
neo-aramaic
aramaic
syriac
urmi
urmia
Instructions to use Selest/MMS_urmi_ASR_model_adapters-only_bad-results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Selest/MMS_urmi_ASR_model_adapters-only_bad-results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Selest/MMS_urmi_ASR_model_adapters-only_bad-results")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Selest/MMS_urmi_ASR_model_adapters-only_bad-results") model = AutoModelForCTC.from_pretrained("Selest/MMS_urmi_ASR_model_adapters-only_bad-results") - Notebooks
- Google Colab
- Kaggle
File size: 221 Bytes
d35c4dc | 1 2 3 4 5 6 7 8 9 10 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0,
"return_attention_mask": true,
"sampling_rate": 16000
}
|