Automatic Speech Recognition
Transformers
Safetensors
English
whisper
child-speech
model-merging
compositional-domain-adaptation
07_scaling_laws
supervised-fine-tuning
Instructions to use balaji1312/whisper_base_sft_ogi_script_0_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balaji1312/whisper_base_sft_ogi_script_0_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="balaji1312/whisper_base_sft_ogi_script_0_2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("balaji1312/whisper_base_sft_ogi_script_0_2") model = AutoModelForSpeechSeq2Seq.from_pretrained("balaji1312/whisper_base_sft_ogi_script_0_2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 80, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |