---
language:
  - kk
  - ru
  - uz
  - en
license: cc-by-nc-4.0
tags:
  - automatic-speech-recognition
  - nemo
  - fastconformer
  - ctc
  - multilingual
library_name: nemo
pipeline_tag: automatic-speech-recognition
---

# FastConformer Multilingual ASR

A multilingual automatic speech recognition model supporting **Kazakh, Russian, Uzbek, and English**. Built on NVIDIA NeMo's FastConformer-CTC architecture.

## Languages

| Language | Code |
|----------|------|
| Kazakh | `kk` |
| Russian | `ru` |
| Uzbek (Latin) | `uz` |
| English | `en` |

## Results

Full test set (76,739 samples):

| Language | Samples | CER | WER |
|----------|---------|-----|-----|
| Russian | 10,203 | 2.34% | 9.16% |
| Kazakh | 33,964 | 8.27% | 14.09% |
| Uzbek | 16,184 | 7.10% | 28.82% |
| English | 16,388 | 9.53% | 22.29% |
| **Overall** | **76,739** | **7.73%** | **16.86%** |

## Usage

### Requirements

```bash
pip install nemo_toolkit[asr]
```

### Transcribe Audio Files

```python
import nemo.collections.asr as nemo_asr

model = nemo_asr.models.ASRModel.restore_from("fastconformer_multilingual.nemo")
model.freeze()

transcriptions = model.transcribe(["audio.wav"])
print(transcriptions[0])
```

### Real-Time Streaming Transcription

```python
import nemo.collections.asr as nemo_asr
import sounddevice as sd
import numpy as np
import queue
import tempfile
import os
import soundfile as sf

SAMPLE_RATE = 16000
CHUNK_SEC = 3
CHUNK_SAMPLES = SAMPLE_RATE * CHUNK_SEC

model = nemo_asr.models.ASRModel.restore_from("fastconformer_multilingual.nemo")
model.freeze()

audio_queue = queue.Queue()


def audio_callback(indata, frames, time, status):
    audio_queue.put(indata[:, 0].copy())


def transcribe_stream():
    buffer = np.array([], dtype=np.float32)
    with sd.InputStream(
        samplerate=SAMPLE_RATE,
        channels=1,
        callback=audio_callback,
        blocksize=SAMPLE_RATE,
    ):
        print("Listening... (Ctrl+C to stop)")
        while True:
            chunk = audio_queue.get()
            buffer = np.concatenate([buffer, chunk])
            if len(buffer) >= CHUNK_SAMPLES:
                tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
                sf.write(tmp.name, buffer[:CHUNK_SAMPLES], SAMPLE_RATE)
                result = model.transcribe([tmp.name])
                text = result[0] if isinstance(result[0], str) else result[0].text
                if text.strip():
                    print(f"> {text}")
                os.unlink(tmp.name)
                buffer = buffer[CHUNK_SAMPLES:]


if __name__ == "__main__":
    transcribe_stream()
```

### Batch Transcription

```python
import nemo.collections.asr as nemo_asr
from pathlib import Path

model = nemo_asr.models.ASRModel.restore_from("fastconformer_multilingual.nemo")
model.freeze()

audio_files = list(Path("audio_dir").glob("*.wav"))
transcriptions = model.transcribe([str(f) for f in audio_files], batch_size=32)

for path, text in zip(audio_files, transcriptions):
    t = text if isinstance(text, str) else text.text
    print(f"{path.name}: {t}")
```

## Model Details

- **Architecture**: FastConformer-CTC
- **Framework**: NVIDIA NeMo
- **Audio**: 16kHz mono WAV

## Limitations

- Optimized for clear speech; performance may degrade on noisy audio
- No punctuation or capitalization in output
- Language is auto-detected, not explicitly specified

## License

This model is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial use only.