Instructions to use jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729") model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729: direct link, hf CLI and curl.
- Browser
- Download file 645 Bytes
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https://huggingface.co/jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729/resolve/main/README.md
- Command line
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hf download hf://jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729/README.md
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curl -L -o README.md https://huggingface.co/jonatasgrosman/exp_w2v2t_sv-se_unispeech-ml_s729/resolve/main/README.md
645 Bytes
metadata
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- sv-SE
datasets:
- mozilla-foundation/common_voice_7_0
exp_w2v2t_sv-se_unispeech-ml_s729
Fine-tuned microsoft/unispeech-large-multi-lingual-1500h-cv for speech recognition using the train split of Common Voice 7.0 (sv-SE). When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.