mevine-0.1

Mevine 0.1

A 107M-parameter Conformer–RNN-T speech recognition model, trained from scratch — no distillation, no pretrained encoder, no fine-tuning of an existing checkpoint.

Open weights, non-commercial research use only. See LICENSE — the restriction is inherited from the training corpus, not a preference. A commercially licensable variant trained without the restricted data is planned.

Results

Open ASR Leaderboard methodology: Whisper EnglishTextNormalizer, corpus-level WER (total edits ÷ total reference words).

Throughput

RTFx is audio-seconds decoded per wall-second. Greedy decoding throughout.

Usage

import torch
from transformers import AutoModel

model_path = "theaviv/mevine-0.1"
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if device == "cuda" else torch.float32

model = AutoModel.from_pretrained(
    model_path, trust_remote_code=True, dtype=torch_dtype
).to(device).eval()

print(model.transcribe("audio.wav"))

trust_remote_code=True is required: Mevine's architecture is not part of transformers, so the two modules that define it (configuration_mevine.py, modeling_mevine.py) ship in this repo and are loaded from it. Read them — they are the entire model, ~600 lines with no dependencies beyond the list below.

transcribe accepts a file path in any format torchaudio reads, or a 1-D 16 kHz mono float32 tensor. Audio of any length works: clips longer than 30 s are windowed automatically, with cuts nudged into pauses so words are not sliced in half. Pass chunk_s=0 to force a single window.

For batch or frame-level work, call the model directly — model(mel, mel_len) returns token ids, and model.feature_extractor / model.load_audio / model.tokenizer are the pieces transcribe composes.

Requires transformers>=5.0, torch, torchaudio, tokenizers, soundfile, safetensors. Nothing else.

Model

The encoder is offline and bidirectional. Streaming mode is not available as part of open weights release.

Training data

Read the licence column. SPGISpeech is distributed under a Kensho research/internal-use agreement that restricts redistribution and prohibits commercial use, and Earnings-22 is share-alike. Those terms flow through to these weights. This is disclosed rather than buried: it is why the model is non-commercial, and it is stated in full in LICENSE §3.

GigaSpeech is not in the training corpus — it is gated and contributed 0 hours — which is visible in the results as the weakest column relative to the others.

Limitations

• English only. No other language was trained or evaluated.
• No streaming. The encoder attends bidirectionally over the whole utterance.
• Uneven across domains. Read and prepared speech (LibriSpeech, VoxPopuli, SPGI) are much stronger than spontaneous, noisy or conversational audio (GigaSpeech, Earnings-22). The gap tracks what the corpus contains, not what the architecture can do.
• No punctuation or casing. Output is lowercase, unpunctuated text.
• No speaker diarisation, no timestamps.
• Not evaluated for fairness across accents, dialects or demographics. Do not deploy where that matters without measuring it yourself.
• ASR output contains errors. Do not use for medical, legal, safety-critical or surveillance purposes.

Training

The dev-WER curve was still descending when the compute budget ran out — 200k steps was a budget, not convergence.

Commercial use

These weights are research-only for the corpus reason above, not because the technology is unavailable. A commercially licensable variant — same architecture, same training system, retrained without the restricted data — is in progress.

What is actually on offer is the system rather than this checkpoint: the corpus pipeline, the evaluation harness and the training loop were built to be pointed at a specific domain and re-run, which is what moves WER on audio that a general model handles badly. Mevine 0.1 is the evidence that the loop works end to end, from random initialisation.

If you have an audio workload you would want a model tuned for, open a discussion on this repo.

Citation

@misc{{mevine2026,
  title  = {{Mevine 0.1: a from-scratch 107M Conformer-RNN-T for English ASR}},
  author = {{Dozorets, Aviv}},
  year   = {{2026}},
  note   = {{Open weights, non-commercial research licence}},
  url    = {{https://huggingface.co/theaviv/mevine-0.1}}
}}

Acknowledgements

Trained on LoquaciousSet (SpeechBrain), Earnings-22 (Rev.com), and SPGISpeech (Kensho). Evaluated with the Open ASR Leaderboard methodology.