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AudenAI/
auden-asr-zh-en

Name: auden-asr-zh-en
Availability: InStock

automatic-speech-recognition

apache-2.0

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`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

python

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:

Language	Data Source	Type	Hours	Total Hours
Chinese (Zh)	WenetSpeech	Open Source	10,005	129,265
	AISHELL-2	Open Source	1,000
	AISHELL-1	Open Source	150
	Common Voice	Open Source	237
	Yodas	Open Source	222
	In-house Data	In-house	117,651
English (En)	Libriheavy	Open Source	45,751	107,626
	Multilingual LibriSpeech (MLS)	Open Source	44,659
	GigaSpeech	Open Source	10,000
	Yodas	Open Source	3,426
	Common Voice	Open Source	1,778
	LibriSpeech	Open Source	960
	VoxPopuli	Open Source	522
	TED-LIUM	Open Source	453
	AMI Corpus	Open Source	77
Code-Switch	TALCS	Open Source	555	8,924
	In-house Data	In-house	8,369

📊 Evaluation

Chinese & English ASR (WER↓, greedy search)

Dataset	WER
librispeech-test-clean	1.81
librispeech-test-other	3.63
fleurs-en	7.41
commonvoice20-en	10.33
fleurs-zh-CN	6.35
commonvoice20-zh-CN	6.63
aishell	1.51
aishell2	2.60
wenet_test_meeting	5.19
wenet_test_net	5.51
kespeech	9.72
talcs	9.86
speechio_0	2.15
speechio_1	0.80
speechio_2	3.10
speechio_3	1.36
speechio_4	2.36
speechio_5	1.70
speechio_6	5.83
speechio_7	4.89
speechio_8	4.73
speechio_9	3.56
speechio_10	3.72
speechio_11	1.65
speechio_12	1.98
speechio_13	3.84
speechio_14	4.51
speechio_15	6.03
speechio_16	3.76
speechio_17	2.77
speechio_18	2.29
speechio_19	2.72
speechio_20	3.19
speechio_21	3.33
speechio_22	3.94
speechio_23	3.72
speechio_24	5.25
speechio_25	3.99
speechio_26	3.84

⚠️ Limitations

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

AudenAI/auden-asr-zh-en

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