# wav2vec2-large-xlsr-53-portuguese by Jonatas Grosman Source: https://savrn.com/models/wav2vec2-large-xlsr-53-portuguese Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## SAVRN's Notes on wav2vec2-large-xlsr-53-portuguese The page states no parameter count, so we publish no memory table: what you get is 2.5 GB of weights across bin and msgpack inside 24 files totalling 3.7 GB. It is a wav2vec2-large CTC model fine-tuned from facebook/wav2vec2-large-xlsr-53 on Portuguese — the Common Voice 6.1 train and validation splits — and it expects 16 kHz input. It draws 5.7M downloads a month. Reported test word-error rate is 11.31, dropping to 9.01 with a language model; character error runs 3.74, or 3.21 with one — the LM plumbing is worth real points. Apache 2.0 clears commercial use. First published 2022-03-02, last updated 2022-12-14, and the XLSR-53 lineage predates the current generation of open speech models, so benchmark newer options before committing a production Portuguese pipeline. SAVRN Research ## Model Card By Jonatas Grosman, published under apache-2.0, revision 634ac655299b. ### Fine-tuned XLSR-53 large model for speech recognition in Portuguese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). 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](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) 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](https://github.com/jonatasgrosman/huggingsound) library: ``` 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: [Read the full model card (373 words)](https://savrn.com/models/wav2vec2-large-xlsr-53-portuguese/card) ## Configuration Architecture Wav2Vec2ForCTC Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 46 Model type wav2vec2 ## Identity and Version Repository jonatasgrosman/wav2vec2-large-xlsr-53-portuguese Publisher Jonatas Grosman Task Speech recognition Modality Audio Library transformers Parameters Not stated by the source Languages pt Revision 634ac655299bcdc46c83bc01da9bab52d2987e4f First published 2022-03-02 Last updated 2022-12-14 ## Files and Weights 24 files, 3.7 GB in total. The weights are 2 files totalling 2.5 GB in bin, msgpack. Weights2 files · 2.5 GB Configuration6 files · 8.7 KB Tokenizer1 file · 430 B Documentation1 file · 5.3 KB Other13 files · 1.2 GB Repository1 file · 736 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 1.3 GB | 5601f7577c1f | | pytorch_model.bin | Weights | 1.3 GB | c244caf8395a | | alphabet.json | Configuration | 278 B | — | | config.json | Configuration | 1.8 KB | — | | eval.py | Configuration | 6.2 KB | — | | language_model/attrs.json | Configuration | 78 B | — | | preprocessor_config.json | Configuration | 262 B | — | | special_tokens_map.json | Configuration | 85 B | — | | README.md | Documentation | 5.3 KB | — | | full_eval.sh | Other | 1.4 KB | — | | language_model/lm.binary | Other | 1.2 GB | 85fcd2ff940b | | language_model/unigrams.txt | Other | 8.1 MB | b1b261381b8d | | log_mozilla-foundation_common_voice_6_0_pt_test_predictions.txt | Other | 226.9 KB | — | | log_mozilla-foundation_common_voice_6_0_pt_test_predictions_greedy.txt | Other | 227.0 KB | — | | log_mozilla-foundation_common_voice_6_0_pt_test_targets.txt | Other | 228.0 KB | — | | log_speech-recognition-community-v2_dev_data_pt_validation_predictions.txt | Other | 121.8 KB | — | | log_speech-recognition-community-v2_dev_data_pt_validation_predictions_greedy.txt | Other | 122.6 KB | — | | log_speech-recognition-community-v2_dev_data_pt_validation_targets.txt | Other | 121.9 KB | — | | mozilla-foundation_common_voice_6_0_pt_test_eval_results.txt | Other | 50 B | — | | mozilla-foundation_common_voice_6_0_pt_test_eval_results_greedy.txt | Other | 50 B | — | | speech-recognition-community-v2_dev_data_pt_validation_eval_results.txt | Other | 49 B | — | | speech-recognition-community-v2_dev_data_pt_validation_eval_results_greedy.txt | Other | 49 B | — | | .gitattributes | Repository | 736 B | — | | vocab.json | Tokenizer | 430 B | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 2.5 GB [Download from Jonatas Grosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Released by Jonatas Grosman through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Trained on (disclosed) common_voice - Trained on (disclosed) mozilla-foundation/common_voice_6_0 ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | Common Voice pt | Task Automatic Speech RecognitionMetric Test CERComparison conditions not established | 3.74 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Common Voice pt | Task Automatic Speech RecognitionMetric Test CER (+LM)Comparison conditions not established | 3.21 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Common Voice pt | Task Automatic Speech RecognitionMetric Test WERComparison conditions not established | 11.31 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Common Voice pt | Task Automatic Speech RecognitionMetric Test WER (+LM)Comparison conditions not established | 9.01 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Robust Speech Event - Dev Data | Task Automatic Speech RecognitionMetric Dev CERComparison conditions not established | 17.93 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Robust Speech Event - Dev Data | Task Automatic Speech RecognitionMetric Dev CER (+LM)Comparison conditions not established | 16.88 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Robust Speech Event - Dev Data | Task Automatic Speech RecognitionMetric Dev WERComparison conditions not established | 42.1 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | | Robust Speech Event - Dev Data | Task Automatic Speech RecognitionMetric Dev WER (+LM)Comparison conditions not established | 36.92 | [jonatasgrosman](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.5 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About wav2vec2-large-xlsr-53-portuguese ### Can I use wav2vec2-large-xlsr-53-portuguese commercially? Yes. wav2vec2-large-xlsr-53-portuguese is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Similar Models Model · Speech recognition ### [wav2vec2-large-xlsr-53-japanese](https://savrn.com/models/wav2vec2-large-xlsr-53-japanese) [Jonatas Grosman](https://savrn.com/model-publishers/jonatasgrosman) Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the train and validation splits of Common Voice 6.1, CSS10 and JSUT. 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: The model can be evaluated as follows on the Japanese test data of Common Voice. In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the… Open weights apache-2.0 transformers [View model](https://savrn.com/models/wav2vec2-large-xlsr-53-japanese) Model · Speech recognition ### [whisperkit-coreml](https://savrn.com/models/whisperkit-coreml) [Argmax](https://savrn.com/model-publishers/argmaxinc) WhisperKit is part of Argmax OSS, an On-device Speech AI SDK for Apple Silicon: https://github.com/argmaxinc/argmax-oss-swift Check out the WhisperKit paper and presentation from ICML 2025: https://icml.cc/virtual/2025/47854 For real-time transcription with speakers and custom vocabulary, check out Argmax Pro SDK: https://www.argmaxinc.com/blog/argmax-sdk-2 Open weights mit whisperkit [View model](https://savrn.com/models/whisperkit-coreml) Model · Speech recognition ### [speaker-diarization-3.1](https://savrn.com/models/speaker-diarization-3-1) [Pyannote](https://savrn.com/model-publishers/pyannote) Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This pipeline is the same as pyannote/speaker-diarization-3.0 except it removes the problematic use of onnxruntime. Both speaker segmentation and embedding now run in pure PyTorch. This should ease deployment and possibly speed up inference. It requires pyannote.audio version 3.1 or higher. It ingests mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance: - stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. - audio files sampled at a different rate are resampled to 16kHz automatically upon loading. 1.… Access requested at publisher mit pyannote-audio [View model](https://savrn.com/models/speaker-diarization-3-1) Model · Speech recognition ### [speaker-diarization-community-1](https://savrn.com/models/speaker-diarization-community-1) [Pyannote](https://savrn.com/model-publishers/pyannote) This pipeline ingests mono audio sampled at 16kHz and outputs speaker diarization. - stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. - audio files sampled at a different rate are resampled to 16kHz automatically upon loading. The main improvements brought by Community-1 are: - improved speaker assignment and counting - simpler reconciliation with transcription timestamps with exclusive speaker diarization - easy offline use (i.e. without internet connection) - (optionally) hosted on pyannoteAI cloud 1. pip install pyannote.audio 3. Create access token at hf.co/settings/tokens. Out of the box, Community-1 is much better than… Access requested at publisher cc-by-4.0 pyannote-audio [View model](https://savrn.com/models/speaker-diarization-community-1) Model · Speech recognition ### [faster-whisper-small](https://savrn.com/models/faster-whisper-small) [Systran](https://savrn.com/model-publishers/systran) This repository contains the conversion of openai/whisper-small to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the computetype option in CTranslate2. For more information about the original model, see its model card. Open weights mit ctranslate2 [View model](https://savrn.com/models/faster-whisper-small) Model · Speech recognition ### [wav2vec2-large-xlsr-53-russian](https://savrn.com/models/wav2vec2-large-xlsr-53-russian) [Jonatas Grosman](https://savrn.com/model-publishers/jonatasgrosman) Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the train and validation splits of Common Voice 6.1 and CSS10. 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: 1. To evaluate on mozilla-foundation/commonvoice60 with split test 2. To evaluate on speech-recognition-community-v2/devdata If you want to cite this model you can use this Open weights apache-2.0 transformers [View model](https://savrn.com/models/wav2vec2-large-xlsr-53-russian) ## Jonatas Grosman [All models and datasets](https://savrn.com/model-publishers/jonatasgrosman) ## Versions - [634ac655299b](https://savrn.com/models/wav2vec2-large-xlsr-53-portuguese/versions/634ac655299b) · current 2026-10-25 ## Explore More - [All speech recognition models](https://savrn.com/models/tasks/speech-recognition) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) - [How the hub is built](https://savrn.com/model-hub/methodology)