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MODEL Listed

wav2vec2-large-xlsr-53-portuguese

The wav2vec2-large-xlsr-53-portuguese model is a robust Automatic Speech Recognition (ASR) tool fine-tuned specifically for the Portuguese language. Built upon Meta's cross-lingual wav2vec 2.0 framework, it leverages massive self-supervised pre-training across 53 languages to achieve high phonetic accuracy even with limited labeled Portuguese data. For developers, this means a reliable pipeline for converting Portuguese audio to text without needing to build a model from scratch. It integrates seamlessly into Hugging Face transformers pipelines, making it straightforward to deploy in transcription services, voice-command interfaces, or accessibility tools. Compared to generic multilingual models, this specialized version offers better word error rates (WER) for Portuguese dialects, providing a more precise output for production-grade NLP workflows.

jonatasgrosmanautomatic speech recognition
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01 / MODEL CARD

Model card

The wav2vec2-large-xlsr-53-portuguese model is a robust Automatic Speech Recognition (ASR) tool fine-tuned specifically for the Portuguese language. Built upon Meta's cross-lingual wav2vec 2.0 framework, it leverages massive self-supervised pre-training across 53 languages to achieve high phonetic accuracy even with limited labeled Portuguese data. For developers, this means a reliable pipeline for converting Portuguese audio to text without needing to build a model from scratch. It integrates seamlessly into Hugging Face transformers pipelines, making it straightforward to deploy in transcription services, voice-command interfaces, or accessibility tools. Compared to generic multilingual models, this specialized version offers better word error rates (WER) for Portuguese dialects, providing a more precise output for production-grade NLP workflows.

Model typeautomatic speech recognition
Providerjonatasgrosman
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: jonatasgrosman/wav2vec2-large-xlsr-53-portuguese
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model jonatasgrosman/wav2vec2-large-xlsr-53-portuguese
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model jonatasgrosman/wav2vec2-large-xlsr-53-portuguese README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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