Automatic Speech Recognition
Transformers
Safetensors
English
whisper
child-speech
model-merging
compositional-domain-adaptation
04_robustness_synthetic_small
headwise-selective-attention
Instructions to use balaji1312/whisper_small_hsa_all_tts_script_0_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balaji1312/whisper_small_hsa_all_tts_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_small_hsa_all_tts_script_0_2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("balaji1312/whisper_small_hsa_all_tts_script_0_2") model = AutoModelForSpeechSeq2Seq.from_pretrained("balaji1312/whisper_small_hsa_all_tts_script_0_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a0215450643efb2abc7912e9da7e3c55d2d8fe31391045177dfbddc65175a9d
- Size of remote file:
- 967 MB
- SHA256:
- f196efcc8edb26b88bdf3936e3661a1b1f8d33b5fc07689fb907694f10accc86
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