Automatic Speech Recognition
Transformers
Safetensors
English
whisper
child-speech
model-merging
compositional-domain-adaptation
02_core_merges_small
supervised-fine-tuning
Instructions to use balaji1312/whisper_small_sft_ogi_spon_6_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balaji1312/whisper_small_sft_ogi_spon_6_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="balaji1312/whisper_small_sft_ogi_spon_6_10")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("balaji1312/whisper_small_sft_ogi_spon_6_10") model = AutoModelForSpeechSeq2Seq.from_pretrained("balaji1312/whisper_small_sft_ogi_spon_6_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1257b01d375fd92427006b4a4a8e8c6e0e0ce1768b53cfd8d1f5360be5d8cfb6
- Size of remote file:
- 967 MB
- SHA256:
- 06d1b77ea268cc6c0278c070f10e0c26c26bffb9b5627ea1da7a4423f86701c2
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