auden-asr-zh-stream: Streaming Chinese ASR

This model card describes AudenAI/auden-asr-zh-stream, a Chinese ASR model that supports both streaming and non-streaming inference. The model is a pruned RNN-T ASR system with a Zipformer encoder. It is a small model (~170M parameters) designed for fast training and inference, optimized for low-latency Mandarin transcription while maintaining strong accuracy on common Chinese ASR benchmarks. The streaming chunk size can be as low as 16 (≈450ms) with only minor degradation; smaller chunk sizes are theoretically supported but performance is not guaranteed. Training uses 138,189 hours of speech data as summarized below.

🔍 What Can This Model Do?

• 🎙️ Streaming Chinese ASR (real-time transcription)
• ⏱️ Low-latency decoding with greedy search
• 🧩 Robust performance across diverse Chinese datasets

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-stream"  # 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) Non-streaming ASR (greedy)
hyp = model.generate(inputs)

Streaming Usage

Streaming setup

Use the streaming script in this repo for real-time style decoding:

python examples/asr/decode_streaming.py \
  --model-dir AudenAI/auden-asr-zh-stream \
  --wav /abs/path/to/test.wav \
  --chunk-size 16 \
  --left-context 128

Common parameters

• chunk-size: internal streaming chunk size (frames, ~10ms per frame)
• left-context: left context frames; larger values improve stability but add latency

Recommended streaming settings

• chunk-size can be as small as 16 (≈450ms effective chunk duration; i.e., (2 * chunk_size + 13) * 10ms). This yields lower latency with only minor degradation in accuracy.
• left-context is configurable; increasing it improves stability/accuracy.

📌 Model Characteristics

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

📚 Training Data Composition

The streaming ASR model is trained on the following data composition:

📊 Evaluation

Chinese ASR (WER↓, non-streaming decoding)

⚠️ Limitations

• Performance depends on audio quality and recording conditions.
• For long-form audio, chunking and post-processing might be required.
• Not designed for safety-critical applications.