huggingface--transformers/docs/source/en/model_doc/glmasr.md

This model was contributed to Hugging Face Transformers on 2025-12-24.

GlmAsr

Overview

GLM-ASR-Nano-2512 is a robust, open-source speech recognition model with 1.5B parameters. Designed for real-world complexity, it outperforms OpenAI Whisper V3 on multiple benchmarks while maintaining a compact size.

Key capabilities include:

• Exceptional Dialect Support Beyond standard Mandarin and English, the model is highly optimized for Cantonese (粤语) and other dialects, effectively bridging the gap in dialectal speech recognition.

• Low-Volume Speech Robustness Specifically trained for "Whisper/Quiet Speech" scenarios. It captures and accurately transcribes extremely low-volume audio that traditional models often miss.

• SOTA Performance Achieves the lowest average error rate (4.10) among comparable open-source models, showing significant advantages in Chinese benchmarks (Wenet Meeting, Aishell-1, etc..).

This model was contributed by Eustache Le Bihan and Yuxuan Zhang. you can check the model card for more details and our github repo.

Usage

Basic usage

# pytest-decorator: transformers.testing_utils.slow, transformers.testing_utils.require_torch
from transformers import AutoModelForSeq2SeqLM, AutoProcessor


processor = AutoProcessor.from_pretrained("zai-org/GLM-ASR-Nano-2512")
model = AutoModelForSeq2SeqLM.from_pretrained("zai-org/GLM-ASR-Nano-2512", device_map="auto")

inputs = processor.apply_transcription_request("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")

inputs = inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
assert len(decoded_outputs) == 1  # nodoc
print(decoded_outputs)

Advanced usage

The processor's apply_transcription_request is equivalent to using the chat template in the following manner:

# pytest-decorator: transformers.testing_utils.slow, transformers.testing_utils.require_torch
from transformers import AutoProcessor, GlmAsrForConditionalGeneration


processor = AutoProcessor.from_pretrained("zai-org/GLM-ASR-Nano-2512")
model = GlmAsrForConditionalGeneration.from_pretrained("zai-org/GLM-ASR-Nano-2512", device_map="auto")

inputs = processor.apply_transcription_request("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")

# which is equivalent to
conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "audio",
                "url": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
            },
            {"type": "text", "text": "Please transcribe this audio into text"},
        ],
    },
]

inputs = processor.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
)

inputs = inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
print(decoded_outputs)

One can also use audio arrays directly:

# pytest-decorator: transformers.testing_utils.slow, transformers.testing_utils.require_torch
from datasets import Audio, load_dataset

from transformers import AutoProcessor, GlmAsrForConditionalGeneration


processor = AutoProcessor.from_pretrained("zai-org/GLM-ASR-Nano-2512")
model = GlmAsrForConditionalGeneration.from_pretrained("zai-org/GLM-ASR-Nano-2512", device_map="auto")

# loading audio directly from dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
audio_array = ds[0]["audio"]["array"]

inputs = processor.apply_transcription_request(audio_array)

inputs = inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
print(decoded_outputs)

Batched inference

You can process multiple audio files at once:

# pytest-decorator: transformers.testing_utils.slow, transformers.testing_utils.require_torch
import torch

from transformers import AutoProcessor, GlmAsrForConditionalGeneration


checkpoint_name = "zai-org/GLM-ASR-Nano-2512"
processor = AutoProcessor.from_pretrained(checkpoint_name)

conversation = [
    [
        {
            "role": "user",
            "content": [
                {
                    "type": "audio",
                    "url": "https://huggingface.co/datasets/eustlb/audio-samples/resolve/main/bcn_weather.mp3",
                },
                {"type": "text", "text": "Please transcribe this audio into text"},
            ],
        },
    ],
    [
        {
            "role": "user",
            "content": [
                {
                    "type": "audio",
                    "url": "https://huggingface.co/datasets/eustlb/audio-samples/resolve/main/obama2.mp3",
                },
                {"type": "text", "text": "Please transcribe this audio into text"},
            ],
        },
    ],
]

model = GlmAsrForConditionalGeneration.from_pretrained(checkpoint_name, device_map="auto")

inputs = processor.apply_chat_template(
    conversation, tokenize=True, add_generation_prompt=True, return_dict=True
).to(model.device, dtype=model.dtype)

inputs_transcription = processor.apply_transcription_request(
    [
        "https://huggingface.co/datasets/eustlb/audio-samples/resolve/main/bcn_weather.mp3",
        "https://huggingface.co/datasets/eustlb/audio-samples/resolve/main/obama2.mp3",
    ],
).to(model.device, dtype=model.dtype)

for key in inputs:  # doc-builder: ignore-bare-assert
    assert torch.equal(inputs[key], inputs_transcription[key])

outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)

decoded_outputs = processor.batch_decode(
    outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True
)

EXPECTED_OUTPUT = [
    "Yesterday it was thirty five degrees in Barcelona, but today the temperature will go down to minus twenty degrees.",
    "This week, I traveled to Chicago to deliver my final farewell address to the nation, following in the tradition of presidents before me. It was an opportunity to say thank you. Whether we've seen eye to eye or rarely agreed at all, my conversations with you, the American people, in living rooms and schools, at farms and on factory floors, at diners and on distant military outposts, all these conversations are what have kept me honest, kept me inspired, and kept me going. Every day, I learned from you. You made me a better president, and you made me a better man. Over the",
]
assert decoded_outputs == EXPECTED_OUTPUT

GlmAsrEncoderConfig

autodoc GlmAsrEncoderConfig

GlmAsrConfig

autodoc GlmAsrConfig

GlmAsrPreTrainedModel

autodoc GlmAsrPreTrainedModel - forward

GlmAsrProcessor

autodoc GlmAsrProcessor - call

GlmAsrEncoder

autodoc GlmAsrEncoder - forward

GlmAsrModel

autodoc GlmAsrModel - forward

GlmAsrForConditionalGeneration

autodoc GlmAsrForConditionalGeneration - forward