jonatasgrosman/wav2vec2-large-xlsr-53-persian
automatic-speech-recognition model by jonatasgrosman
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Model card & documentation
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Persian using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint The model can be used directly (without a language model) as follows... Using the HuggingSound library: Writing your own inference script: The model can be evaluated as follows on the Persian test data of Common Voice. Test Result: In the table below I report the…
Read the full model card ↗ · Preview checked 2026-10-29T15:33:36.262Z
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Model overview
jonatasgrosman/wav2vec2-large-xlsr-53-persian is a automatic-speech-recognition model repository published by jonatasgrosman on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- automatic-speech-recognition
- Library
- transformers
- Recent downloads (30 days)
- 1,065,051
- Cumulative likes
- 40
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- Wav2Vec2ForCTC
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2022-03-02T23:29:05.000Z
- Last modified
- 2022-12-14T01:57:01.000Z
Compatibility and lineage
Reported languages: fa
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:32.899Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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