granite-4.0-1b-speech

Name: granite-4.0-1b-speech
Author: ibm-granite

by ibm-granite

#1 Open ASR Leaderboard at 1B: edge-deployable multilingual transcription

120Kdl/month

1Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

ibm-granite/granite-4.0-1b-speech

Feature URI

mixpeek://transcription@v1/ibm_granite_40_1b_speech_v1

Overview

Granite 4.0 1B Speech is the smallest model to reach #1 on the HuggingFace Open ASR Leaderboard. At just 1B parameters, it achieves 1.42% WER on LibriSpeech Clean and 5.52% average WER across benchmarks, while running at 280x realtime factor on GPU.

It supports English and Japanese with keyword list biasing for domain-specific vocabulary. The compact size makes it ideal for edge deployment, serverless functions, and cost-sensitive pipelines where Whisper Large v3 (1.5B) is too heavy. On Mixpeek, it serves as the default transcription model for latency-sensitive and high-volume audio processing.

Architecture

Compact encoder-decoder (1B parameters) optimized for throughput. Supports keyword biasing via attention-based shallow fusion. English + Japanese language support.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so granite-4.0-1b-speech 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.