Automatic Speech Recognition
Transformers
Safetensors
Arabic
wav2vec2-bert
audio-frame-classification
Generated from Trainer
arabic
quran
speech-segmentation
Instructions to use obadx/recitation-segmenter-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use obadx/recitation-segmenter-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="obadx/recitation-segmenter-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioFrameClassification processor = AutoProcessor.from_pretrained("obadx/recitation-segmenter-v2") model = AutoModelForAudioFrameClassification.from_pretrained("obadx/recitation-segmenter-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from obadx/recitation-segmenter-v2: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/obadx/recitation-segmenter-v2/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://obadx/recitation-segmenter-v2@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/obadx/recitation-segmenter-v2/resolve/refs%2Fpr%2F1/preprocessor_config.json
275 Bytes
| { | |
| "feature_extractor_type": "SeamlessM4TFeatureExtractor", | |
| "feature_size": 80, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 1, | |
| "processor_class": "Wav2Vec2BertProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| } | |