Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Swahili
wav2vec2
Generated from Trainer
hf-asr-leaderboard
model_for_talk
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results (legacy)
Instructions to use Akashpb13/Swahili_xlsr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akashpb13/Swahili_xlsr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akashpb13/Swahili_xlsr")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akashpb13/Swahili_xlsr") model = AutoModelForCTC.from_pretrained("Akashpb13/Swahili_xlsr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Akashpb13/
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
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It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
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value: 0.02884228669922436
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# Akashpb13/Swahili_xlsr
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - hu dataset.
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It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
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