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Grushashunyalabsai/
qwen3-asr-rakuten-multilingual-18lang

Name: qwen3-asr-rakuten-multilingual-18lang
Availability: InStock

automatic-speech-recognition

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Qwen3-ASR Rakuten Multilingual (18 Languages)

Fine-tuned Qwen3-ASR model for multilingual automatic speech recognition, optimized for Rakuten customer service use cases.

Supported Languages (18)

English, Chinese, French, Portuguese, Korean, Vietnamese, Hindi, Thai, Indonesian, Tamil, Urdu, Nepali, Uzbek, Mongolian, Sinhala, Burmese, Japanese, Italian

Quick Start

Installation

pip install -U qwen-asr

Inference

python

import torch
from qwen_asr import Qwen3ASRModel

model_path = "Grushashunyalabsai/qwen3-asr-rakuten-multilingual-18lang"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device.startswith("cuda") else torch.float32

asr = Qwen3ASRModel.from_pretrained(
    model_path,
    dtype=dtype,
    device_map=device,
    max_inference_batch_size=16,
    max_new_tokens=440,
)

# Single file (auto-detect language)
results = asr.transcribe(audio="audio.wav")
print(results[0].language)  # e.g. "Japanese"
print(results[0].text)      # transcribed text

# Force a specific language
results = asr.transcribe(audio="audio.wav", language="Japanese")

# From numpy array (16kHz mono)
import numpy as np
audio_array = np.zeros(16000, dtype=np.float32)  # 1 sec
results = asr.transcribe(audio=(audio_array, 16000))

# Batch processing
results = asr.transcribe(
    audio=["file1.wav", "file2.wav", "file3.wav"],
    language=["Japanese", "English", "Hindi"],
)
for r in results:
    print(f"{r.language}: {r.text}")

CLI Inference

bash

# Auto-detect language
python infer.py audio.wav

# Force language
python infer.py audio.wav --language Japanese

# Batch folder to JSONL
python infer.py ./audio_dir --out results.jsonl

Model Details

Base Model: Qwen/Qwen3-ASR-1.7B
Format: SafeTensors (bf16)
Size: ~3.8 GB
Max Audio Duration: 30 seconds per utterance
Sample Rate: 16kHz (automatically resampled)

Training

Hardware: 2x A100 GPUs (DDP)
Data: 1,624,974 utterances across 18 languages
Epochs: 2
Learning Rate: 2e-5 (linear schedule)
Batch Size: 16 (effective)
Gradient Checkpointing: Enabled
Final Train Loss: ~0.10
Max Transcript Length: 500 characters

Audio Input Formats

The model accepts:

File paths: .wav, .flac, .mp3, .m4a, .ogg, .opus, .webm
NumPy arrays: (np.ndarray, sample_rate) tuple
URLs: Direct audio URLs
Batch: List of any of the above

Output Format

python

@dataclass
class ASRTranscription:
    language: str    # e.g. "Japanese", "English", "Chinese,English"
    text: str        # Transcribed text
    time_stamps: Optional[Any]  # Word-level timestamps (if requested)

Files

model.safetensors — Model weights (bf16)
config.json — Model configuration
tokenizer_config.json — Tokenizer configuration
vocab.json — Vocabulary
merges.txt — BPE merges
infer.py — CLI inference script
preprocessor_config.json — Audio preprocessor config