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automatic speech recognition by jonatasgrosman

wav2vec2-large-xlsr-53-german

wav2vec2-large-xlsr-53-german is a German ASR model fine-tuned from Facebook's XLSR-53 large checkpoint on Mozilla Common Voice 6.0. It was contributed during Hugging Face's XLSR fine-tuning week and is listed on the hf-asr-leaderboard, providing a traceable benchmark reference. The Apache 2.0 license and Azure deployment tag make it accessible for both research and production German transcription pipelines.

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From the model card

Fields below are copied from the tags and counters on the HuggingFace repository jonatasgrosman/wav2vec2-large-xlsr-53-german at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
jonatasgrosman
Pipeline tag
automatic-speech-recognition
Library
Transformers
Framework tags
PyTorch, JAX
License tag
apache-2.0 — read the license file in the repo before relying on it
Language tags
German (de)
Datasets declared
common_voice, mozilla-foundation/common_voice_6_0
Downloads (HF counter at last fetch)
457,416
Likes (HF counter at last fetch)
8
Model card
https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german

Use cases

  • Transcribing German speech in customer service or call center audio
  • German-language voice-to-text for accessibility tools
  • Benchmarking XLSR fine-tuning approaches on German Common Voice data
  • Preprocessing German audio for downstream NLP classification or NER

Pros

  • Listed on hf-asr-leaderboard, providing a community-verified benchmark reference
  • Trained on Common Voice 6.0, a well-documented and reproducible dataset
  • Apache 2.0 license allows unrestricted commercial use
  • Supports both PyTorch and JAX inference backends
  • Azure-compatible deployment reduces friction for cloud-based pipelines

Cons

  • Fine-tuned on Common Voice 6.0, which may not represent formal or domain-specific German speech (e.g., legal, medical)
  • XLSR-53 large is a relatively older architecture; newer models may outperform it on WER
  • No explicit handling of Austrian or Swiss German dialect variation
  • Common Voice crowdsourced data introduces recording quality variance that may affect production accuracy
  • Model card benchmarks are from 2021; newer test sets may show performance degradation

Tags

transformerspytorchjaxwav2vec2automatic-speech-recognitionaudiodehf-asr-leaderboardmozilla-foundation/common_voice_6_0robust-speech-eventspeechxlsr-fine-tuning-weekdataset:common_voicedataset:mozilla-foundation/common_voice_6_0doi:10.57967/hf/3583license:apache-2.0model-indexendpoints_compatibledeploy:azureregion:us