stage1a_smoke_data — AuT-ready 128-mel TFRecords (en/zh)

Smoke-scale training data for Stage 1A input audio alignment of a Qwen3-ASR-AuT → MLP → frozen-VL-LLM omni model. Audio is pre-extracted 128-bin log-mel (the Qwen3-ASR AuT frontend: WhisperFeatureExtractor, 16 kHz, hop 160, n_fft 400) so training only needs to run the frozen AuT encoder — no raw-audio decoding at train time.

113,396 samples across 4 sources, stored as GZIP-compressed TFRecords (one file per source shard).

Sources & mix

source	task	lang	samples
LibriSpeech train.clean.100	asr	en	28,539
AISHELL-1 (train)	asr	zh	34,679
AudioCaps (train)	caption	en	45,178
synthetic silence/no-speech	silence	na	5,000

Licenses follow the upstream corpora: LibriSpeech CC-BY-4.0; AISHELL-1 Apache-2.0; AudioCaps CC-BY-NC-4.0 (non-commercial); silence is synthetic. Use accordingly.

TFRecord schema (per tf.train.Example)

feature	type	meaning
mel	bytes	fp16 raw, reshape to [n_mels, n_frames]
n_mels	int64	128
n_frames	int64	mel frames, multiple of 100 (AuT n_window*2 chunk requirement)
feature_len	int64	true valid mel frames (<= n_frames; the tail is zero-pad)
n_audio_tokens	int64	aut_out_lengths(feature_len) = number of AUDIO_PAD placeholders after the AuT encoder (each full 100 mel frames → 13 encoder frames)
text	bytes	utf-8 target: transcript / caption / <no_speech>
task	bytes	asr | caption | silence
lang	bytes	en | zh | na

Load

import tensorflow as tf, numpy as np, glob

FEAT = {
    "mel": tf.io.FixedLenFeature([], tf.string),
    "n_mels": tf.io.FixedLenFeature([], tf.int64),
    "n_frames": tf.io.FixedLenFeature([], tf.int64),
    "feature_len": tf.io.FixedLenFeature([], tf.int64),
    "n_audio_tokens": tf.io.FixedLenFeature([], tf.int64),
    "text": tf.io.FixedLenFeature([], tf.string),
    "task": tf.io.FixedLenFeature([], tf.string),
    "lang": tf.io.FixedLenFeature([], tf.string),
}

def parse(raw):
    e = tf.io.parse_single_example(raw, FEAT)
    return e

files = glob.glob("*.tfrecord.gz")
ds = tf.data.TFRecordDataset(files, compression_type="GZIP").map(parse)
for e in ds.take(1):
    nm, nf = int(e["n_mels"]), int(e["n_frames"])
    mel = np.frombuffer(e["mel"].numpy(), np.float16).reshape(nm, nf)   # [128, n_frames], cast to fp32 for AuT
    print(mel.shape, e["text"].numpy().decode(), int(e["feature_len"]), int(e["n_audio_tokens"]))

At train time: mel[:, :ceil_to_100(feature_len)] → cast fp32 → frozen AuT encoder → 2048-d audio_states → trainable 2-layer MLP → scatter into n_audio_tokens AUDIO_PAD slots of the frozen LLM.

Provenance

Generated by stage1a_data_prep.py + stage1a_npz_to_tfrecord.py (LLaVA-OV2-tpu, branch feat/qwen3-audio-stack). This is smoke-scale for pipeline validation; full-scale Stage 1A uses online mel extraction over the larger recipe (MLS-en, People's Speech, etc.).