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<nav aria-label="Breadcrumbs" class="breadcrumbs"><a href="https://www.funasr.com/en/">FunASR</a><span>/</span><a href="https://www.funasr.com/en/docs/">Docs</a><span>/</span><span>Transformers quickstart</span></nav>
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<header class="docs-title"><h1>Transformers quickstart</h1><a class="source-link" data-source-link="" href="https://github.com/modelscope/FunASR/blob/main/docs/transformers_native.md">View / edit source ↗</a></header>
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<article class="docs-article"><div class="source-title-anchor" id="fun-asr-nano-with-transformers"></div>
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<p><a href="https://www.funasr.com/docs/native-transformers.html">简体中文</a> | English</p>
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<p>Transcribe Chinese, English and Japanese with <code>AutoProcessor</code> and
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<code>AutoModelForSpeechSeq2Seq</code>. <strong>Transformers 5.17.0 is a released package with
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native Fun-ASR-Nano support.</strong> No source checkout, FunASR toolkit or
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checkpoint-provided Python code is needed.</p>
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<p><a href="https://huggingface.co/spaces/FunAudioLLM/Fun-ASR-Nano">Try the Space</a> ·
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<a href="https://colab.research.google.com/github/QwenAudio/Fun-ASR/blob/main/examples/colab/fun_asr_nano_transformers.ipynb">Open the notebook</a> ·
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<a href="https://github.com/QwenAudio/Fun-ASR/tree/main/examples/transformers">Runnable examples</a></p>
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<h2 id="get-your-first-transcript">Get your first transcript</h2>
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<p>Start in an isolated environment. This reproducible installation is for Linux
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x86-64, Python 3.12 and <strong>CPU</strong>; do not upgrade an existing vLLM or FunASR service
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environment in place. Torch and torchaudio must match because the native
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feature extractor uses <code>torchaudio.compliance.kaldi.fbank</code>.</p>
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<pre id="code-0"><code class="language-bash">python3.12 -m venv .venv-funasr-native
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. .venv-funasr-native/bin/activate
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python -m pip install --index-url https://download.pytorch.org/whl/cpu \
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'torch==2.10.0+cpu' 'torchaudio==2.10.0+cpu'
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python -m pip install 'transformers==5.17.0' 'numpy==1.26.4' \
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'librosa==0.11.0' 'soundfile==0.13.1' \
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'huggingface-hub==1.30.0' 'tokenizers==0.23.2'
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python -m pip check
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</code></pre>
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<p>Run this Python example. It downloads about 1.66 GB of model weights on first
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use and transcribes a short, pinned official English sample. CPU float32 is
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explicit; no GPU is needed for this example.</p>
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<!-- native-example: transcribe -->
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<pre id="code-1"><code class="language-python">import torch
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
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torch.set_num_threads(4)
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model_id = "FunAudioLLM/Fun-ASR-Nano-2512-hf"
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revision = "d93b302ee7fd505e1b3576120fc142fc6f7820e1"
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audio = "https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512/resolve/272c57b82523ada6fd87095e955f8e29100979ab/example/en.mp3"
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processor = AutoProcessor.from_pretrained(
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model_id, revision=revision, trust_remote_code=False, token=False
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)
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_id, revision=revision, trust_remote_code=False, token=False,
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dtype=torch.float32,
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).to("cpu").eval()
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inputs = processor.apply_transcription_request(
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audio=audio, language="en",
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processor_kwargs={
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"return_tensors": "pt",
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"audio_kwargs": {"sampling_rate": 16000},
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"text_kwargs": {"padding": True},
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},
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)
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with torch.inference_mode():
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generated = model.generate(**inputs, max_new_tokens=128, do_sample=False)
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new_tokens = generated[:, inputs.input_ids.shape[1]:]
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print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])
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</code></pre>
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<p>The model returns the prompt and generated tokens together. Remove the full
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input tensor width before decoding. For this sample, the observed raw output
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was: “The tribal chieftain called for the boy, and presented him with fifty
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pieces of gold.” This is a functional example, not a quality guarantee.</p>
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<h2 id="your-files-keywords-and-batches">Your files, keywords and batches</h2>
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<p>The <a href="https://github.com/QwenAudio/Fun-ASR/tree/main/examples/transformers">command-line example</a>
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adds explicit input validation, sample-rate conversion and completion checks.
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Clone the <strong>QwenAudio/Fun-ASR code repository</strong> (not an HF weights repository),
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install the <a href="https://github.com/QwenAudio/Fun-ASR/tree/main/examples/transformers">independent native requirements</a>,
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and run these commands from its root directory:</p>
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<pre id="code-2"><code class="language-bash">python examples/transformers/transcribe.py recording.wav --language zh --keywords 开放时间
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python examples/transformers/transcribe.py chinese.wav english.wav --language zh en
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</code></pre>
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<p>It averages multichannel audio to mono and resamples to 16 kHz with <code>soxr_hq</code>;
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it never overwrites the original file. It accepts 1-4 files with at most
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60 seconds of total audio. Longer recordings, empty input and non-finite samples
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are rejected, not silently trimmed.</p>
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<p>For your own application, pass a waveform, local path or supported URL to
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<code>processor.apply_transcription_request</code>. A raw waveform must have its true
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sample rate; do not label 48 kHz samples as 16 kHz. Use <code>language="zh"</code>,
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<code>"en"</code> or <code>"ja"</code>. For a batch, use an audio list, matching language list and
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<code>text_kwargs={"padding": True}</code>. Preserve input order when decoding.</p>
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<p><code>keywords</code> supplies vocabulary hints and <code>prompt</code> supplies context. They are
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not enforced vocabulary and are not the toolkit's <code>hotword</code> parameter. In our
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Chinese sample, adding <code>开放时间</code> did not correct the raw output <code>开饭时间</code>.
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Use representative recordings and human references to assess quality.</p>
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<p>The short example bounds generation at 128 new tokens. Missing EOS can indicate
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truncation; increasing the limit does not prove coverage of a long recording.
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The CLI reports <code>reached_eos</code> and exits unsuccessfully for missing EOS or empty
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text. EOS itself is not proof that all words were recognized.</p>
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<h2 id="run-the-cli-on-cuda">Run the CLI on CUDA</h2>
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<p>Follow the <a href="https://github.com/QwenAudio/Fun-ASR/tree/main/examples/transformers#cuda-an-isolated-tested-recipe">isolated CUDA installation</a>,
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including its separate <code>requirements-gpu.txt</code>, before running this from the
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QwenAudio/Fun-ASR code repository with your local audio files:</p>
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<pre id="code-3"><code class="language-bash">python examples/transformers/transcribe.py chinese.wav english.wav --language zh en --device cuda --dtype bfloat16
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</code></pre>
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<p>On <strong>2026-09-10</strong>, Linux/Python 3.12, H100 80 GB, driver <strong>550.127.08</strong>,
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torch/torchaudio <strong>2.11.0+cu128</strong> and Transformers <strong>5.17.0</strong> passed eight
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float32/BF16 English, Chinese, keyword and padded mixed-batch functional cases.
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The updated CLI also passed GPU single-file/batch and default CPU regression.
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These are not accuracy, minimum-VRAM or serving-capacity benchmarks; the Chinese
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keyword error described above remains. Other GPUs and float16 were not tested.</p>
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<p>No device flags still means CPU float32. Explicit CUDA fails when unavailable,
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and BF16 requires device support; no silent CPU fallback. The model and inputs
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move together without casting integer token IDs. Output records device and dtype.
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An attention-dispatch warning was observed; successful inference does not verify
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every component's kernel or imply Flash Attention performance. Hosted GPU
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Space/Colab execution has not been verified by these local checks.</p>
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<h2 id="from-an-example-to-a-service">From an example to a service</h2>
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<table>
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<thead>
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<tr>
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<th>Need</th>
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<th>Checkpoint and interface</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Native Transformers in Python</td>
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<td><code>FunAudioLLM/Fun-ASR-Nano-2512-hf</code>, <code>AutoProcessor</code> + <code>AutoModelForSpeechSeq2Seq</code></td>
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</tr>
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<tr>
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<td>FunASR pipelines and HTTP adapters</td>
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<td><code>FunAudioLLM/Fun-ASR-Nano-2512</code>, <code>funasr.AutoModel</code>; <a href="https://www.funasr.com/en/docs/deployment-matrix.html">deployment matrix</a></td>
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</tr>
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<tr>
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<td>Native vLLM</td>
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<td><code>FunAudioLLM/Fun-ASR-Nano-2512-vllm</code>; <a href="https://www.funasr.com/en/docs/official-native-vllm.html">native vLLM guide</a></td>
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</tr>
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<tr>
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<td>C++ / edge</td>
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<td>Converted GGUF files; <a href="https://github.com/modelscope/FunASR/blob/main/runtime/llama.cpp/README.md">llama.cpp</a></td>
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</tr>
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</tbody>
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</table>
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<p>These are different artifacts, not interchangeable model IDs. Native
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Transformers support does not add an HTTP server, queue, streaming protocol,
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word timestamps or speaker identities. This native export omits the CTC
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timestamp branch. The base Nano checkpoint covers zh/en/ja and Chinese
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dialects/accents; the <strong>31-language MLT</strong> checkpoint is separate.</p>
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<p>For transcription with anonymous speakers and segment timestamps, see the
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third-party OpenMOSS <a href="https://www.funasr.com/en/docs/moss-transcribe-diarize.html">MOSS guide</a>. For other tasks,
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use the <a href="https://www.funasr.com/en/docs/model-zoo.html">Model Zoo</a>. Validate GPU dtype, attention
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backends, memory, concurrency and quality on the actual target hardware.</p>
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<h2 id="troubleshooting-without-loading-weights">Troubleshooting without loading weights</h2>
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<p>If loading fails, check the active interpreter, <code>transformers.__version__</code>,
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the <code>-hf</code> model ID and model revision first. Version 5.16.1 lacks this model;
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upgrade in the new environment, not an unrelated running service. The native
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classes live in <code>transformers.models.fun_asr_nano</code>.</p>
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<p>This optional synthetic-silence check downloads the processor, not model weights:</p>
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<!-- native-example: processor -->
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<pre id="code-4"><code class="language-python">import numpy as np
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from transformers import AutoProcessor
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model_id = "FunAudioLLM/Fun-ASR-Nano-2512-hf"
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revision = "d93b302ee7fd505e1b3576120fc142fc6f7820e1"
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processor = AutoProcessor.from_pretrained(
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model_id, revision=revision, trust_remote_code=False, token=False
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)
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inputs = processor.apply_transcription_request(
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audio=np.zeros(16000, dtype=np.float32), language="en",
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processor_kwargs={
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"return_tensors": "pt",
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"audio_kwargs": {"sampling_rate": 16000},
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},
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)
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print({name: tuple(value.shape) for name, value in inputs.items()})
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</code></pre>
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<p>Reject empty recordings and batches before calling the processor. Synthetic
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preprocessing success is <strong>not an accuracy or capacity result</strong>.</p>
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<h2 id="reproducibility-and-sources">Reproducibility and sources</h2>
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<p>Verified on <strong>2026-09-09</strong> using the actual released 5.17.0 wheel, Python 3.12.3,
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matching torch/torchaudio 2.10.0+cpu, float32 and four Torch threads on an Intel
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Xeon Platinum 8480+. Official English URL, Chinese waveform, Chinese keywords
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|
and a padded Chinese/English batch all returned non-empty text and EOS before
|
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128 tokens. These two short public samples do not establish CER/WER, GPU
|
|
compatibility or serving capacity.</p>
|
|
<p>The public native revision is
|
|
<code>d93b302ee7fd505e1b3576120fc142fc6f7820e1</code>; it resolves to
|
|
<code>FunAsrNanoForConditionalGeneration</code> with <code>trust_remote_code=False</code>.
|
|
<code>model.safetensors</code> is 1,659,773,320 bytes, SHA256
|
|
<code>1bbb6dcc5d8b75084a399d48c4d4b0f3aa1d3f09f2616ae40ed2c4fba03d89c9</code>.
|
|
Audio samples come from the original repository's
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|
<a href="https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512/tree/272c57b82523ada6fd87095e955f8e29100979ab/example">fixed example directory</a>,
|
|
not an invented <code>-hf/example</code> path.</p>
|
|
<p><a href="https://github.com/huggingface/transformers/pull/46180">PR #46180</a> merged as
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|
<code>fc501343edfccdc840eb8594a6cafa8185c2de53</code> before the stable package was
|
|
published. Earlier source-only instructions described that interval; new
|
|
installations should use the released package above.
|
|
See the <a href="https://huggingface.co/docs/transformers/v5.17.0/en/model_doc/fun_asr_nano">official 5.17.0 model documentation</a>
|
|
and <a href="https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512-hf">native model card</a>.</p>
|
|
<p>Keep versions, input hashes and raw results with application evaluations.
|
|
Never attach private recordings or credentials to a public issue.</p></article>
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<nav aria-label="Adjacent documents" class="docs-next">
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<a href="https://www.funasr.com/en/docs/installation.html">Installation & environment checks →</a> </nav>
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</div>
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<aside class="docs-toc"><strong>On this page</strong><div class="toc">
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<ul>
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<li><a href="#fun-asr-nano-with-transformers">Fun-ASR-Nano with Transformers</a><ul>
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<li><a href="#get-your-first-transcript">Get your first transcript</a></li>
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<li><a href="#your-files-keywords-and-batches">Your files, keywords and batches</a></li>
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<li><a href="#run-the-cli-on-cuda">Run the CLI on CUDA</a></li>
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<li><a href="#from-an-example-to-a-service">From an example to a service</a></li>
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<li><a href="#troubleshooting-without-loading-weights">Troubleshooting without loading weights</a></li>
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<li><a href="#reproducibility-and-sources">Reproducibility and sources</a></li>
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</ul>
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</li>
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