wav2vec2-large-960h
by facebook
Self-supervised speech representations for automatic speech recognition
facebook/wav2vec2-large-960hmixpeek://transcription@v1/facebook_wav2vec2_large_v1Overview
Wav2Vec 2.0 learns speech representations from raw audio through self-supervised pre-training, then fine-tunes with a small amount of labeled data. The 960h variant is fine-tuned on the full LibriSpeech dataset.
On Mixpeek, Wav2Vec2 provides an alternative to Whisper for English transcription, with strong performance on clear speech and a smaller memory footprint.
Architecture
CNN feature encoder (7 convolutional layers) followed by a 24-layer Transformer. Self-supervised pre-training uses contrastive loss over quantized speech representations. Fine-tuned with CTC loss.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so wav2vec2-large-960h runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// The model produces text, so it lands in payload. Give the
// collection a text vector index and embed that text to make it
// searchable rather than only filterable.
payload: { extracted_text: modelOutput, source_key: "archive/2026/asset-00412" },
vectors: { "text-embedding": embeddingOfModelOutput },
},
],
}),
},
);
// Managed alternative, if this exact model is not the requirement:
// universal_extractor@v1 runs google/gemini-embedding-2
// (3072-d) over a bucket, with no inference of your own.Capabilities
- Self-supervised pre-training on unlabeled audio
- Strong English ASR performance
- Raw waveform input (no spectrogram needed)
- Efficient fine-tuning with limited labeled data
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| LibriSpeech (test-clean) | WER | 2.7% | Baevski et al., 2020: Table 5 |
| LibriSpeech (test-other) | WER | 5.2% | Baevski et al., 2020: Table 5 |
Performance
Common Pipeline Companions
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Specification
Research Paper
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
arxiv.orgBuild a pipeline with wav2vec2-large-960h
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Run it on your own data, free