Whisper-large-v3-turbo Russian — Code-Switching variant
Specialized variant of coriollon/whisper-large-v3-turbo-russian
optimized for Russian speech with embedded English tech terms ("Открой Python и сделай git push").
When to use this vs the standard model
| Use case | Recommended model |
|---|---|
| Pure Russian dictation | coriollon/whisper-large-v3-turbo-russian (9.43% AGG WER) |
| RU+EN code-switching (IT dictation, tech podcasts) | this model |
This variant trades ~2 pp on pure Russian WER for a massive code-switching gain.
Code-switching benchmark (540 RU sentences with EN tech terms)
| Metric | Standard RU model | This model | Δ |
|---|---|---|---|
| WER | 24.64% | 9.08% | −15.56 pp |
| Term accuracy¹ | 12.59% | 74.26% | +61.67 pp |
¹ Term accuracy = fraction of utterances where the English tech term (e.g. "Python", "GitHub", "Docker") appears verbatim in latin script in the transcript.
Pure-Russian regression
| Dataset | Standard RU | This model | Δ |
|---|---|---|---|
| Common Voice 21 RU | 5.33% | 6.87% | +1.54 |
| RuLibriSpeech | 8.50% | 8.57% | +0.07 |
| Sberdevices Golos farfield | 11.05% | 10.38% | −0.67 |
| Sberdevices Golos crowd | 10.15% | 9.81% | −0.34 |
| SOVA RuDevices | 13.61% | 14.87% | +1.26 |
| Podlodka Speech | 10.85% | (noisy n=20) | — |
| AGG (N=200) | ~9.4% | ~11.8% | +2.3 |
Usage
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch
repo = "coriollon/whisper-large-v3-turbo-russian-codeswitch"
processor = WhisperProcessor.from_pretrained(repo)
model = WhisperForConditionalGeneration.from_pretrained(repo, torch_dtype=torch.float16).to("cuda")
inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
feats = inputs.input_features.to("cuda", dtype=torch.float16)
ids = model.generate(feats, language="ru", task="transcribe", num_beams=5)
print(processor.batch_decode(ids, skip_special_tokens=True)[0])
Pre-quantized faster-whisper variant
| Folder | Quantization | Size |
|---|---|---|
ct2_int8_float16/ |
int8 weights + fp16 compute | 782 MB |
from huggingface_hub import snapshot_download
from faster_whisper import WhisperModel
ct2_path = snapshot_download(
repo_id="coriollon/whisper-large-v3-turbo-russian-codeswitch",
allow_patterns="ct2_int8_float16/*",
)
model = WhisperModel(f"{ct2_path}/ct2_int8_float16", device="cuda", compute_type="int8_float16")
segments, _ = model.transcribe("audio.wav", language="ru", beam_size=5)
How it was built
LoRA r=32 (q+k+v+o+fc1+fc2, 27.85M params) trained on:
- 2,160 RU sentences with EN tech terms (Silero TTS-bootstrapped, 50 templates × 53 terms × 5 voices)
- 5,000 pure-RU anti-forgetting samples
- 2,000 EN anti-forgetting samples
Reference labels in mixed script ("Открой Python") teach the model to natively output latin tokens for English terms instead of cyrillic transliteration ("питон").
Limitations
- Pure-RU WER is ~2 pp worse than the standard model. Don't use this model if you don't need code-switching.
- Training data was TTS-generated (Silero v4 RU) — real human pronunciation may differ slightly
- Currently covers 53 common tech/brand terms; rare terms may still appear in cyrillic
License
Apache 2.0 (inherited from base Whisper model).