Automatic Speech Recognition
Transformers
Safetensors
Bengali
whisper
bengali
bangla
speech-recognition
punctuation-restoration
Instructions to use SayedShaun/bengali-whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SayedShaun/bengali-whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SayedShaun/bengali-whisper-medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SayedShaun/bengali-whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("SayedShaun/bengali-whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete punct/12layers/config.json with huggingface_hub
Browse files- punct/12layers/config.json +0 -38
punct/12layers/config.json
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{
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"embedding_size": 768,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "O",
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"1": "PERIOD",
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"2": "COMMA",
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"3": "QUESTION"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"O": 0,
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"PERIOD": 1,
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"COMMA": 2,
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"QUESTION": 3
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.30.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 197285,
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"base_model": "google/muril-base-cased"
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}
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