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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/funasr_nano_2512_decode_int8lin_n63_s1/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/funasr_nano_audio_encoder_fp16w32_l500/funasr_nano_audio_encoder_fp16w32_l500.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text
+gpu-pipelined/funasr_nano_2512_decode_int8lin_n63_s1/funasr_nano_2512_decode_int8lin_n63_s1.aimodel/main.mlirb filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

@@ -0,0 +1,24 @@
+Fun-ASR-Nano-2512-CoreAI — NOTICE
+
+This repository redistributes, in Apple Core AI `.aimodel` form, the weights of
+FunAudioLLM/Fun-ASR-Nano-2512 (Fun-ASR, Tongyi Lab / Alibaba Group), licensed under the
+Apache License, Version 2.0 (see LICENSE). The upstream model repository declares
+`license: apache-2.0` in its model card and carries no LICENSE file; the LICENSE text here is
+the one shipped by the official vLLM packaging FunAudioLLM/Fun-ASR-Nano-2512-vllm, whose
+`model.safetensors` (sha256 96dfbec48282dd24d3334369a01e9e909f321ee39a1b0003c528c5379f68c1a6)
+is bit-identical to the official `model.pt` (revision 272c57b82523ada6fd87095e955f8e29100979ab)
+and is the source of every tensor converted here.
+
+Fun-ASR-Nano's text decoder is a fine-tuned Qwen3-0.6B (Qwen Team, Alibaba Cloud, Apache-2.0);
+its tokenizer files are redistributed unchanged. The audio encoder is the SenseVoice SAN-M
+encoder and adaptor trained by FunAudioLLM. The CTC decoder configured in the upstream
+`config.yaml` has no weights in the released checkpoint and is not part of this port.
+
+Conversion: mlboydaisuke (john-rocky), 2026-09. Code, gates and fixtures:
+https://github.com/john-rocky/coreai-model-zoo (conversion/funasr_nano, models/funasr-nano).
+Modifications to the network as exported: fixed-shape re-authoring for 30 s windows; the
+encoder stored in float16 and computed in float32; the decoder's linear weights quantized to
+int8 (per-block 32, symmetric); the decoder's residual stream scaled by 1/4 with the matching
+RMSNorm epsilon (a numerically equivalent transformation that keeps float16 activations in range).
+
+Keep this NOTICE together with the LICENSE file when redistributing.

README.md

@@ -0,0 +1,102 @@
+---
+license: apache-2.0
+library_name: coreai
+pipeline_tag: automatic-speech-recognition
+base_model: FunAudioLLM/Fun-ASR-Nano-2512
+language:
+  - zh
+  - en
+  - ja
+  - yue
+tags: [core-ai, coreaikit, funasr, fun-asr, sensevoice, san-m, qwen3, asr, speech-recognition, hotwords, on-device, apple]
+base_model_relation: quantized
+---
+
+Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's `coreai-torch` (LLMs: `coreai.llm.export`) into `.aimodel` bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol ([apple-silicon-llm-bench](https://github.com/john-rocky/apple-silicon-llm-bench), macOS 27 beta, 2026-06).
+
+# Fun-ASR-Nano-2512 — Core AI
+
+[`FunAudioLLM/Fun-ASR-Nano-2512`](https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512) (Tongyi Lab,
+Apache-2.0, 985M) converted to Apple **Core AI** `.aimodel` bundles: speech-to-text for **Chinese
+(with dialects and accents, Cantonese included), English and Japanese**, punctuation and inverse text
+normalization built in, an optional **hotword list** in the prompt. Encoder + decoder **1.2 GB**.
+
+- SenseVoice **SAN-M encoder** (50 + 20 layers, FSMN memory) + 2-block adaptor → `audio_embeds` rows
+- **fine-tuned Qwen3-0.6B** decoder (int8 linears, tied fp16 head), driven by the Core AI high-level
+  engine with the audio rows bound as a static input
+- one 30 s window per call (500 LFR frames → 63 audio rows); longer clips are windowed on the host
+
+## Numbers (2026-09, macOS 27, M4 Max GPU; fixtures = the 5 upstream `example/*.mp3` + FLEURS test en_us / cmn_hans_cn / ja_jp, 50 each, CC BY 4.0)
+
+Oracle = the publisher's `funasr` 1.4.16 in fp32, dither 0, greedy.
+
+| | result |
+| --- | --- |
+| end-to-end token agreement with the fp32 oracle, 155 clips | 150/155 exact; the 5 others diverge only where the oracle's own top-2 gap is 0.007–0.033 (knife-edge, runner-up chosen); 0 divergences above the 0.1 floor |
+| WER en / CER zh / CER ja, port vs oracle | 0.34 % / 0.00 % / 0.07 % |
+| WER en / CER zh / CER ja vs FLEURS reference, oracle → port | 5.08 → 5.34 % / 6.86 → 6.86 % / 6.95 → 6.92 % |
+| encoder graph vs oracle (per-row cosine, 155 clips) | mean 0.99999988, min 0.9999982 |
+| Swift host (CoreAIKit) vs the Python engine | identical ids on 155/155 |
+
+| speed (CoreAIKit, Release, medians over the 155 clips) | encoder / prefill / decode | RTF median / p90 | first load → later loads |
+| --- | --- | --- | --- |
+| M4 Max, GPU, macOS 27 (26A428) | 40 ms / 123 ms / 2.78 ms per token | 0.022 / 0.028 | 3.6 s → 0.55 s |
+| iPhone 18 Pro, GPU, iOS 27 (24A437), device JIT | 146 ms / 393 ms / 8.2 ms per token | 0.075 / 0.094 | 5.1 s → 0.9 s |
+
+On the phone a 13.6 s clip takes 0.91 s (nominal thermal state); two minutes of back-to-back clips
+raise the state to *fair* and the encoder to 225 ms. Peak footprint 385–411 MB. No AOT compile needed.
+
+The FLEURS rows are our measurement of the fp32 model and of this port on 50 utterances per language
+under one normalizer; they are not the publisher's benchmark table.
+
+## Files
+
+```
+gpu-pipelined/funasr_nano_2512_decode_int8lin_n63_s1/     decoder: <name>.aimodel + metadata.json + tokenizer/   (759 MB)
+gpu-pipelined/funasr_nano_audio_encoder_fp16w32_l500/     encoder: funasr_nano_audio_encoder_fp16w32_l500.aimodel (450 MB)
+config.json · config.yaml · preprocessor_config.json      the source model's configuration
+LICENSE · NOTICE                                           Apache-2.0 text + attribution
+```
+
+Both bundles are JIT `.aimodel`s: macOS and iPhone specialize them on the first load, so one subtree
+serves both platforms. The encoder stores float16 weights and computes in float32 (its `feats` /
+`mask` inputs and `audio_embeds` output are float32). The decoder graph runs its residual stream at
+1/4 with the matching RMSNorm eps (`metadata.json`: `residual_scale`, `rmsnorm_eps_residual`) — an
+exact transformation that keeps the fine-tuned Qwen3's 125k activation at position 0 inside float16.
+
+## Host contract
+
+```
+wav 16 kHz mono → kaldi fbank 80 (hamming 25/10 ms, pre-emphasis 0.97, DC removal, ×32768, log floor FLT_EPSILON,
+                  dither 0) → LFR 7/6 → feats[L,560], zero-padded to [1,500,560] + mask[1,500]
+encoder → audio_embeds[63,1024]; keep rows [:N], N = ceil(L/8)
+prompt ids = enc("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n语音转写：")   (18 ids)
+           + N ids of 151936 + slot
+           + enc("<|im_end|>\n<|im_start|>assistant\n")                                                        (5 ids)
+greedy to EOS (151645 or 151643), ≤ 512 tokens; text = decode(skip_special_tokens), "/sil" → " ", spaces collapsed
+hotwords: prepend "请结合上下文信息，更加准确地完成语音转写任务。如果没有相关信息，我们会留空。\n\n\n**上下文信息：**\n\n\n热词列表：[a, b]\n"
+language: "语音转写成{中���|英文|日文}："   itn off: "语音转写，不进行文本规整："
+```
+
+## Use it (CoreAIKit)
+
+```swift
+import CoreAIKit
+
+let asr = try await KitFunASRModel(catalog: "fun-asr-nano-2512")        // downloads this repo
+let samples = try AudioFile.pcm16kMono(url)
+let result = try await asr.transcribe(samples: samples)                  // result.text
+let biased = try await asr.transcribe(samples: samples, hotwords: ["开放时间"])
+```
+
+Conversion code, oracle, fixtures and gates: [coreai-model-zoo `conversion/funasr_nano`](https://github.com/john-rocky/coreai-model-zoo/tree/main/conversion/funasr_nano);
+card with the lessons: [`models/funasr-nano`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/funasr-nano/README.md).
+Other runtimes for this model: MLX (`mlx-community/Fun-ASR-Nano-2512-*`), ONNX / sherpa-onnx
+(`csukuangfj/*funasr-nano*`), GGUF (`FunAudioLLM/Fun-ASR-Nano-GGUF`, FunASR's llama.cpp runtime).
+
+## License
+
+Apache-2.0 (the source model's card states it; the LICENSE text is the one the official vLLM packaging
+`FunAudioLLM/Fun-ASR-Nano-2512-vllm` ships, whose weights are bit-identical to the official `model.pt`).
+See NOTICE for attribution. The CTC decoder configured upstream has no weights in the released
+checkpoint, so this port has no timestamps.

SHA256SUMS

@@ -0,0 +1,17 @@
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+109b815cc321ff586c7f200ef1c422f815479e10808fe871991d900618db074d  NOTICE
+174cf936286a0ef87aa3ef7f298e7e7d11577f8896197b0a0a0ad2d0dd4f749f  README.md
+5e72ae4baf304d65bfdf0e779be19817a71f7d65441b096f1dcff377c63ed0e5  config.json
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+007aac6d52b5ecf5e75052330489138a6a8940d68cf3e9723c502e8093ab1b38  preprocessor_config.json

config.json

@@ -0,0 +1,52 @@
+{
+  "architectures": [
+    "FunASRForConditionalGeneration"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "max_source_positions": 1500,
+  "decoder_start_token_id": 99999,
+  "audio_encoder": "SenseVoiceEncoderSmall",
+  "audio_encoder_conf": {
+      "output_size": 512,
+      "attention_heads": 4,
+      "linear_units": 2048,
+      "num_blocks": 50,
+      "tp_blocks": 20,
+      "dropout_rate": 0.1,
+      "positional_dropout_rate": 0.1,
+      "attention_dropout_rate": 0.1,
+      "input_layer": "pe",
+      "pos_enc_class": "SinusoidalPositionEncoder",
+      "normalize_before": true,
+      "kernel_size": 11,
+      "sanm_shfit": 0,
+      "selfattention_layer_type": "sanm",
+      "freeze": true,
+      "freeze_layer_num": -1,
+      "feat_permute": true
+  }
+}

config.yaml

@@ -0,0 +1,146 @@
+model: FunASRNano
+model_conf:
+  lsm_weight: 0.1
+  length_normalized_loss: true
+audio_encoder: SenseVoiceEncoderSmall
+audio_encoder_conf:
+  output_size: 512
+  attention_heads: 4
+  linear_units: 2048
+  num_blocks: 50
+  tp_blocks: 20
+  dropout_rate: 0.1
+  positional_dropout_rate: 0.1
+  attention_dropout_rate: 0.1
+  input_layer: pe
+  pos_enc_class: SinusoidalPositionEncoder
+  normalize_before: true
+  kernel_size: 11
+  sanm_shfit: 0
+  selfattention_layer_type: sanm
+  freeze: true
+  freeze_layer_num: -1
+  feat_permute: true
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+  hub: hf
+  freeze: true
+  llm_dtype: bf16
+  init_param_path: Qwen3-0.6B
+  use_lora: false
+  lora_conf:
+    freeze_lora: true
+    task_type: CAUSAL_LM
+    r: 16
+    lora_alpha: 32
+    lora_dropout: 0.05
+    bias: none
+    target_modules:
+      - q_proj
+      - v_proj
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+audio_adaptor_conf:
+  downsample_rate: 1
+  use_low_frame_rate: true
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+  llm_dim: 1024
+  encoder_dim: 512
+  n_layer: 2
+  freeze: true
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+  llm_dim: 512
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+  freeze: false
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+  dropout_rate: 0.0
+  ctc_type: builtin
+  reduce: true
+  ignore_nan_grad: true
+frontend: WavFrontend
+frontend_conf:
+  fs: 16000
+  window: hamming
+  n_mels: 80
+  frame_length: 25
+  frame_shift: 10
+  lfr_m: 7
+  lfr_n: 6
+  cmvn_file: null
+train_conf:
+  use_lora: ${llm_conf.use_lora}
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 2
+  keep_nbest_models: 200
+  log_interval: 100
+  effective_save_name_excludes:
+    - llm.
+  resume: true
+  validate_interval: 2000
+  save_checkpoint_interval: 2000
+  avg_nbest_model: 100
+  use_bf16: false
+  use_deepspeed: true
+  deepspeed_config: null
+  save_init_model: false
+optim: adamw
+optim_conf:
+  lr: 5.0e-06
+  weight_decay: 0.0
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 2500
+dataset: FunASR
+dataset_conf:
+  index_ds: FunASR
+  batch_sampler: BatchSampler
+  batch_type: token
+  batch_size: 6000
+  max_token_length: 3500
+  shuffle: true
+  sort_size: 1024
+  batch_size_scale_ratio_max: 2
+  num_workers: 4
+  audio_adaptor_downsample_rate: ${audio_adaptor_conf.downsample_rate}
+  audio_encoder_downsample_rate: 6
+  data_split_num: 256
+  batch_size_sample_max: 10
+  retry: 2000
+  batch_size_token_max: 6000
+  max_source_length: 12000
+  max_target_length: 2048
+  prompt_classes: MultiContextPrompt
+  prompt_conf:
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+    min_neg_hotwords_num: 0
+    use_hist: false
+    use_one_pass_result: true
+    use_hotwords: true
+    use_asr_hotwords: true
+    chinese_hotwords_list: null
+    english_hotwords_list: null
+  ctc_tokenizer: SenseVoiceTokenizer
+  ctc_target_normalize: true
+  ctc_tokenizer_conf:
+    vocab_path: null
+    is_multilingual: true
+    num_languages: 8749
+  min_source_length: 10
+  batch_size_scale_threshold: 3000
+  use_dynamic_output_ratio: 0.0
+tokenizer: HuggingfaceTokenizer
+tokenizer_conf:
+  init_param_path: ${llm_conf.init_param_path}
+enable_tf32: true
+debug: false
+train_data_set_list: null
+valid_data_set_list: null
+init_param: null
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+  "assets": {
+    "main": "funasr_nano_2512_decode_int8lin_n63_s1.aimodel"
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+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

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+}
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