auden-asr-zh-en: Chinese-English ASR

This model card describes AudenAI/auden-asr-zh-en, a bilingual ASR model for Chinese and English speech. It is a pruned RNN-T ASR system with a Zipformer encoder and is designed for non-streaming transcription only. It is a small model (~170M parameters) designed for fast training and inference. Training uses 245,815 hours of Chinese, English, and code-switching data summarized below, and the model targets robust accuracy across common Chinese and English benchmarks.

🔍 What Can This Model Do?

• 🎙️ Chinese ASR (Mandarin transcription)
• 🌍 English ASR (English transcription)
• 🧩 Robust performance across mixed Chinese/English data

Quick Start

Non-streaming Usage

from auden.auto.auto_model import AutoModel

# 1) Load a model checkpoint directory (contains config.json + weights)
model_dir = "AudenAI/auden-asr-zh-en"  # HF repo id or exported directory
model = AutoModel.from_pretrained(model_dir)
model = model.to("cuda")
model.eval()

# 2) Prepare input features (x, x_lens). If you have raw audio, you can use
#    model.speech_encoder.extract_feature(wav) to get (x, x_lens).
x, x_lens = ...  # Tensor shapes: (B, T, F), (B,)

inputs = (x, x_lens)
# Alternatively, you can pass WAV inputs directly:
# - List of WAV paths (str):
#   inputs = ["/abs/a.wav", "/abs/b.wav"]
# - List of mono waveforms (Tensor/ndarray), 16 kHz:
#   inputs = [torch.randn(16000*5), torch.randn(16000*3)]

# 3) ASR (greedy)
hyp = model.generate(inputs)

📌 Model Characteristics

• Model ID: AudenAI/auden-asr-zh-en
• Input: Raw audio waveform (16 kHz recommended)
• Output: Chinese and English transcription
• Decoding: Greedy search (non-streaming)
• Task: transcribe

📚 Training Data Composition

This model is trained using Chinese, English, and code-switching data only:

📊 Evaluation

Chinese & English ASR (WER↓, greedy search)

⚠️ Limitations

• Performance depends on audio quality and recording conditions.
• Not designed for safety-critical applications.