Name: wav2vec2 large xlsr 53 portuguese
Author: jonatasgrosman

Overview

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.

Highlights

High-accuracy Portuguese speech-to-text transcription
Built on Meta's cross-lingual XLS-R architecture
Seamless integration via Hugging Face Transformers
Apache-2.0 license for flexible commercial deployment
Optimized for lower word error rates in Portuguese

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-portuguese")
tokenizer = AutoTokenizer.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-portuguese")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download jonatasgrosman/wav2vec2-large-xlsr-53-portuguese config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')
tokenizer = AutoTokenizer.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

Download a single file to a local folder (e.g. README.md into ./dir)

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

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese.git

To skip LFS large-file downloads, use:

Skip LFS

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

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'jonatasgrosman/wav2vec2-large-xlsr-53-portuguese')

Full Documentation

来源: HuggingFace

---
language: pt
license: apache-2.0
datasets:

common_voice

mozilla-foundation/common_voice_6_0

metrics:

wer

cer

tags:

audio

automatic-speech-recognition

hf-asr-leaderboard

mozilla-foundation/common_voice_6_0

pt

robust-speech-event

speech

xlsr-fine-tuning-week

model-index:

name: XLSR Wav2Vec2 Portuguese by Jonatas Grosman

results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 11.31
- name: Test CER
type: cer
value: 3.74
- name: Test WER (+LM)
type: wer
value: 9.01
- name: Test CER (+LM)
type: cer
value: 3.21
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Dev WER
type: wer
value: 42.1
- name: Dev CER
type: cer
value: 17.93
- name: Dev WER (+LM)
type: wer
value: 36.92
- name: Dev CER (+LM)
type: cer
value: 16.88
---

Fine-tuned XLSR-53 large model for speech recognition in Portuguese

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese 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

Usage

The model can be used directly (without a language model) as follows...

Using the HuggingSound library:

python

from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-portuguese")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)

Writing your own inference script:

python

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "pt"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese"
SAMPLES = 10
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
Preprocessing the datasets.
We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)

| Reference | Prediction |
| ------------- | ------------- |
| NEM O RADAR NEM OS OUTROS INSTRUMENTOS DETECTARAM O BOMBARDEIRO STEALTH. | NEMHUM VADAN OS OLTWES INSTRUMENTOS DE TTÉÃN UM BOMBERDEIRO OSTER |
| PEDIR DINHEIRO EMPRESTADO ÀS PESSOAS DA ALDEIA | E DIR ENGINHEIRO EMPRESTAR AS PESSOAS DA ALDEIA |
| OITO | OITO |
| TRANCÁ-LOS | TRANCAUVOS |
| REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA | REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA |
| O YOUTUBE AINDA É A MELHOR PLATAFORMA DE VÍDEOS. | YOUTUBE AINDA É A MELHOR PLATAFOMA DE VÍDEOS |
| MENINA E MENINO BEIJANDO NAS SOMBRAS | MENINA E MENINO BEIJANDO NAS SOMBRAS |
| EU SOU O SENHOR | EU SOU O SENHOR |
| DUAS MULHERES QUE SENTAM-SE PARA BAIXO LENDO JORNAIS. | DUAS MIERES QUE SENTAM-SE PARA BAICLANE JODNÓI |
| EU ORIGINALMENTE ESPERAVA | EU ORIGINALMENTE ESPERAVA |

Evaluation

1. To evaluate on mozilla-foundation/common_voice_6_0 with split test

bash

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset mozilla-foundation/common_voice_6_0 --config pt --split test

2. To evaluate on speech-recognition-community-v2/dev_data

bash

python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset speech-recognition-community-v2/dev_data --config pt --split validation --chunk_length_s 5.0 --stride_length_s 1.0

Citation

If you want to cite this model you can use this:

bibtex

@misc{grosman2021xlsr53-large-portuguese,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {P}ortuguese},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese}},
  year={2021}
}