Instructions to use ambujm22/mspeecht5_asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ambujm22/mspeecht5_asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ambujm22/mspeecht5_asr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ambujm22/mspeecht5_asr") model = AutoModelForSpeechSeq2Seq.from_pretrained("ambujm22/mspeecht5_asr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm_char.model from ambujm22/mspeecht5_asr: direct link, hf CLI and curl.
- Browser
- Download file 238 kB
-
https://huggingface.co/ambujm22/mspeecht5_asr/resolve/main/spm_char.model
- Command line
-
hf download hf://ambujm22/mspeecht5_asr/spm_char.model
-
curl -L -o spm_char.model https://huggingface.co/ambujm22/mspeecht5_asr/resolve/main/spm_char.model
238 kB
- Xet hash:
- 4dc82f60ce6b29d9a35208f0d2bdecdcf961d82cfe1d43b92d72506ab8d06829
- Size of remote file:
- 238 kB
- SHA256:
- 7fcc48f3e225f627b1641db410ceb0c8649bd2b0c982e150b03f8be3728ab560
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