Instructions to use jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728: direct link, hf CLI and curl.
- Browser
- Download file 633 Bytes
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https://huggingface.co/jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728/resolve/main/README.md
- Command line
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hf download hf://jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728/README.md
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curl -L -o README.md https://huggingface.co/jonatasgrosman/exp_w2v2t_ja_unispeech-ml_s728/resolve/main/README.md
633 Bytes
metadata
language:
- ja
license: apache-2.0
tags:
- automatic-speech-recognition
- ja
datasets:
- mozilla-foundation/common_voice_7_0
exp_w2v2t_ja_unispeech-ml_s728
Fine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition using the train split of Common Voice 7.0 (ja). When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.