Initial upload: INT4 ONNX (encoder + prefill + step) + tokenizer + examples + inference.py

.gitattributes

@@ -33,3 +33,14 @@ 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
+examples/distortion.wav filter=lfs diff=lfs merge=lfs -text
+examples/dropout.wav filter=lfs diff=lfs merge=lfs -text
+examples/echo.wav filter=lfs diff=lfs merge=lfs -text
+examples/far_field.wav filter=lfs diff=lfs merge=lfs -text
+examples/mixed.wav filter=lfs diff=lfs merge=lfs -text
+examples/noise.wav filter=lfs diff=lfs merge=lfs -text
+examples/obstructed.wav filter=lfs diff=lfs merge=lfs -text
+examples/recording.wav filter=lfs diff=lfs merge=lfs -text
+onnx/audio_encoder_int4.onnx.data filter=lfs diff=lfs merge=lfs -text
+onnx/decoder_prefill_int4.onnx.data filter=lfs diff=lfs merge=lfs -text
+onnx/decoder_step_int4.onnx.data filter=lfs diff=lfs merge=lfs -text

chat_template.json

@@ -0,0 +1 @@
+{"chat_template": "{%- set ns = namespace(system_text=\"\") -%}\n{%- for m in messages -%}\n  {%- if m.role == 'system' -%}\n    {%- if m.content is string -%}\n      {%- set ns.system_text = ns.system_text + m.content -%}\n    {%- else -%}\n      {%- for c in m.content -%}\n        {%- if c.type == 'text' and (c.text is defined) -%}\n          {%- set ns.system_text = ns.system_text + c.text -%}\n        {%- endif -%}\n      {%- endfor -%}\n    {%- endif -%}\n  {%- endif -%}\n{%- endfor -%}\n\n{%- set ns2 = namespace(audio_tokens=\"\") -%}\n{%- for m in messages -%}\n  {%- if m.content is not string -%}\n    {%- for c in m.content -%}\n      {%- if c.type == 'audio' or ('audio' in c) or ('audio_url' in c) -%}\n        {%- set ns2.audio_tokens = ns2.audio_tokens + \"<|audio_start|><|audio_pad|><|audio_end|>\" -%}\n      {%- endif -%}\n    {%- endfor -%}\n  {%- endif -%}\n{%- endfor -%}\n\n{{- '<|im_start|>system\\n' + (ns.system_text if ns.system_text is string else '') + '<|im_end|>\\n' -}}\n{{- '<|im_start|>user\\n' + ns2.audio_tokens + '<|im_end|>\\n' -}}\n{%- if add_generation_prompt -%}\n{{- '<|im_start|>assistant\\n' -}}\n{%- endif -%}"}

config.json

@@ -0,0 +1,221 @@
+{
+    "architectures": [
+      "Qwen3ASRForConditionalGeneration"
+    ],
+    "model_type": "qwen3_asr",
+    "support_languages": [
+      "Chinese",
+      "English",
+      "Cantonese",
+      "Arabic",
+      "German",
+      "French",
+      "Spanish",
+      "Portuguese",
+      "Indonesian",
+      "Italian",
+      "Korean",
+      "Russian",
+      "Thai",
+      "Vietnamese",
+      "Japanese",
+      "Turkish",
+      "Hindi",
+      "Malay",
+      "Dutch",
+      "Swedish",
+      "Danish",
+      "Finnish",
+      "Polish",
+      "Czech",
+      "Filipino",
+      "Persian",
+      "Greek",
+      "Romanian",
+      "Hungarian",
+      "Macedonian"
+    ],
+    "thinker_config": {
+      "model_type": "qwen3_asr",
+      "architectures": [
+        "Qwen3ASRForConditionalGeneration"
+      ],
+      "audio_config": {
+        "_name_or_path": "",
+        "activation_dropout": 0,
+        "activation_function": "gelu",
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+        "id2label": {
+          "0": "LABEL_0",
+          "1": "LABEL_1"
+        },
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+        "is_decoder": false,
+        "is_encoder_decoder": false,
+        "label2id": {
+          "LABEL_0": 0,
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+        "length_penalty": 1.0,
+        "max_length": 20,
+        "max_source_positions": 1500,
+        "min_length": 0,
+        "model_type": "qwen3_asr_audio_encoder",
+        "n_window": 50,
+        "n_window_infer": 800,
+        "no_repeat_ngram_size": 0,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_hidden_layers": 24,
+        "num_mel_bins": 128,
+        "num_return_sequences": 1,
+        "output_attentions": false,
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+        "temperature": 1.0,
+        "tf_legacy_loss": false,
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+        "tie_word_embeddings": true,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
+        "torchscript": false,
+        "typical_p": 1.0,
+        "use_bfloat16": false
+      },
+      "audio_end_token_id": 151670,
+      "audio_start_token_id": 151669,
+      "audio_token_id": 151676,
+      "dtype": "bfloat16",
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+      "text_config": {
+        "_name_or_path": "",
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+        "architectures": null,
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+        "head_dim": 128,
+        "hidden_act": "silu",
+        "hidden_size": 2048,
+        "id2label": {
+          "0": "LABEL_0",
+          "1": "LABEL_1"
+        },
+        "initializer_range": 0.02,
+        "intermediate_size": 6144,
+        "is_decoder": false,
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+        "max_position_embeddings": 65536,
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+        "model_type": "qwen3",
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+        "num_attention_heads": 16,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_hidden_layers": 28,
+        "num_key_value_heads": 8,
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+        "rms_norm_eps": 1e-06,
+        "rope_scaling": {
+          "interleaved": true,
+          "mrope_interleaved": true,
+          "mrope_section": [
+            24,
+            20,
+            20
+          ],
+          "rope_type": "default",
+          "type": "default"
+        },
+        "rope_theta": 1000000,
+        "sep_token_id": null,
+        "suppress_tokens": null,
+        "task_specific_params": null,
+        "temperature": 1.0,
+        "tf_legacy_loss": false,
+        "tie_encoder_decoder": false,
+        "tie_word_embeddings": true,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
+        "torchscript": false,
+        "typical_p": 1.0,
+        "use_bfloat16": false,
+        "use_cache": true,
+        "vocab_size": 151936
+      }
+    },
+    "transformers_version": "4.57.6"
+  }
+  

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generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": [
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+  ],
+  "pad_token_id": 151643,
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inference.py

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+"""Standalone Mega-ASR inference using the INT4 ONNX models.
+
+This script demonstrates how to run Mega-ASR end-to-end from raw audio
+using only the ONNX models in this repo plus the qwen-asr tokenizer.
+
+Requirements:
+    pip install onnxruntime numpy soundfile transformers qwen-asr
+
+Usage:
+    python inference.py --audio examples/noise.wav
+"""
+from __future__ import annotations
+
+import argparse
+import re
+import sys
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+import soundfile as sf
+
+
+HERE = Path(__file__).parent
+
+
+def _ort(path, providers=("CPUExecutionProvider",)):
+    so = ort.SessionOptions()
+    so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+    return ort.InferenceSession(str(path), so, providers=list(providers))
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--audio", required=True, type=Path)
+    ap.add_argument("--max-new-tokens", type=int, default=80)
+    ap.add_argument("--providers", default="cuda,cpu",
+                    help="Comma-separated ORT providers, e.g. cuda,cpu")
+    args = ap.parse_args()
+
+    providers = []
+    for p in args.providers.split(","):
+        p = p.strip().lower()
+        if p == "cuda": providers.append("CUDAExecutionProvider")
+        elif p == "cpu": providers.append("CPUExecutionProvider")
+        elif p == "webgpu": providers.append("WebGpuExecutionProvider")
+
+    # 1) Tokenizer + processor (needs HF transformers + qwen-asr installed)
+    from transformers import AutoTokenizer
+    from qwen_asr.core.transformers_backend.processing_qwen3_asr import Qwen3ASRProcessor
+    tokenizer = AutoTokenizer.from_pretrained(str(HERE))
+    processor = Qwen3ASRProcessor.from_pretrained(str(HERE))
+
+    # 2) Read audio + build mel features
+    audio, sr = sf.read(str(args.audio))
+    if sr != 16000:
+        import librosa
+        audio = librosa.resample(audio.astype(np.float32), orig_sr=sr, target_sr=16000)
+
+    # The processor expects a prompt with the audio placeholder. Build it as
+    # the Qwen3-ASR chat template would.
+    prompt = (
+        "<|im_start|>system\\nYou are a speech recognition model.<|im_end|>\\n"
+        "<|im_start|>user\\nDetect the language and recognize the speech: "
+        "<|audio_start|><|audio_pad|><|audio_end|><|im_end|>\\n"
+        "<|im_start|>assistant\\n"
+    )
+    inputs = processor(text=[prompt], audio=[audio], return_tensors="np", padding=True)
+    text_ids = inputs["input_ids"]                 # (1, L)
+    attention_mask = inputs["attention_mask"]      # (1, L)
+    input_features = inputs["input_features"]      # (1, 128, T_mel)
+
+    # 3) Audio encoder
+    T_mel = input_features.shape[-1]
+    if T_mel > 3000:
+        input_features = input_features[..., :3000]
+        T_mel = 3000
+    mel_padded = np.pad(input_features.astype(np.float32),
+                         ((0, 0), (0, 0), (0, 3000 - T_mel)))
+    enc = _ort(HERE / "onnx" / "audio_encoder_int4.onnx", providers)
+    audio_embeds = enc.run(["audio_embeds"], {"mel": mel_padded})[0]   # (1, 390, 2048)
+    real_chunks = (T_mel + 99) // 100
+    last_chunk_mel = T_mel - (real_chunks - 1) * 100
+    real_audio_frames = (real_chunks - 1) * 13 + (last_chunk_mel + 7) // 8
+    audio_embeds = audio_embeds[:, :real_audio_frames]                 # (1, N, 2048)
+
+    # 4) Embed text tokens + scatter audio into placeholder positions
+    # The embedding table is published as embed_tokens.npy in the repo
+    # (extracted from the original Qwen3-ASR weights for portability).
+    embed_path = HERE / "onnx" / "embed_tokens.npy"
+    if embed_path.exists():
+        embed_w = np.load(embed_path).astype(np.float16)
+    else:
+        raise FileNotFoundError(
+            "embed_tokens.npy missing — re-extract from the source PT model."
+        )
+    inputs_embeds = embed_w[text_ids[0]][None]                          # (1, L, hidden)
+    audio_token_id = tokenizer.convert_tokens_to_ids("<|audio_pad|>")
+    placeholder_mask = (text_ids == audio_token_id)                     # (1, L)
+    # Replace placeholder embeddings with audio_embeds[0] in order
+    placeholder_idx = np.where(placeholder_mask[0])[0]
+    n_replace = min(len(placeholder_idx), audio_embeds.shape[1])
+    inputs_embeds[0, placeholder_idx[:n_replace]] = audio_embeds[0, :n_replace]
+
+    # 5) Position ids (1D)
+    pos_ids = np.arange(text_ids.shape[1])[None].astype(np.int64)
+
+    # 6) Prefill ONNX
+    prefill = _ort(HERE / "onnx" / "decoder_prefill_int4.onnx", providers)
+    feeds = {
+        "inputs_embeds": inputs_embeds.astype(np.float16),
+        "attention_mask": attention_mask.astype(np.int64),
+        "position_ids": pos_ids,
+    }
+    out = prefill.run(None, feeds)
+    logits, *past_kvs = out
+    eos = tokenizer.eos_token_id
+
+    # 7) Greedy decode with step ONNX
+    step = _ort(HERE / "onnx" / "decoder_step_int4.onnx", providers)
+    num_layers = 28
+    cur_len = inputs_embeds.shape[1]
+    gen = []
+    nid = int(np.argmax(logits[0, -1, :]))
+    gen.append(nid)
+
+    for _ in range(args.max_new_tokens - 1):
+        if nid == eos: break
+        new_embed = embed_w[nid][None, None].astype(np.float16)
+        new_attn = np.ones((1, cur_len + 1), dtype=np.int64)
+        new_pos = np.array([[cur_len]], dtype=np.int64)
+        f = {"inputs_embeds": new_embed,
+             "attention_mask": new_attn,
+             "position_ids": new_pos}
+        for i in range(num_layers):
+            f[f"past.{i}.key"] = past_kvs[2 * i]
+            f[f"past.{i}.value"] = past_kvs[2 * i + 1]
+        out = step.run(None, f)
+        logits, *past_kvs = out
+        nid = int(np.argmax(logits[0, -1, :]))
+        gen.append(nid)
+        cur_len += 1
+
+    text = tokenizer.decode(gen, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    print(f"TRANSCRIPTION: {text}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

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preprocessor_config.json

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+{
+  "chunk_length": 30,
+  "dither": 0.0,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "Qwen3ASRProcessor",
+  "return_attention_mask": true
+}

tokenizer_config.json

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+  "add_prefix_space": false,
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