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"version": "v2",
"input": {
"shape": "[T, 512]",
"dtype": "float32",
"what": "EmbeddingGemma 2 audio soft tokens (embed_audio output), 25 per second"
},
"context": "bidirectional within the window",
"architecture": {
"name": "a",
"kind": "transformer encoder from scratch",
"layers": 6,
"d_model": 512,
"heads": 8,
"ffn": 2048,
"activation": "gelu",
"norm": "pre-LayerNorm",
"positions": "rotary (base 10000)",
"input": "LayerNorm -> Linear 512->512",
"output": "LayerNorm -> Linear 512->768",
"params": 19573762
},
"output": {
"vector": "[T, 768] L2-normalized",
"blank_logit": "[T]"
},
"logits": "scale * cos(vector, table[w]) for w in vocab; blank from blank_logit",
"scale": 216.3509979248047,
"vocab": "vocab.txt",
"table": {
"file": "word_table.npy",
"dtype": "float16",
"shape": "[200000, 768]",
"prompt": "task: search result | query: ",
"normalized": true
},
"decode": "greedy argmax over N+1, collapse repeats, drop blank",
"window": {
"seconds": 30,
"left_overlap_tokens": 96,
"right_extra_tokens": 1,
"note": "encoder is causal; the 83 tokens at the start of a window and its last token differ from a continuous run (fixtures/long-speckled-band: soft_tokens_w30 vs soft_tokens_continuous)"
},
"weights": {
"file": "reader.pt",
"format": "torch state_dict, float32; training matmuls in TF32 (A100), evaluation in fp32",
"code": "train/model.py Reader(arch, n_vocab, k, layers)",
"keys": "body.* (reader body), blank.weight [1, 768], blank.bias [1], log_scale (scale = exp(log_scale))"
},
"n_vocab": 200000,
"wer": {
"dev-clean": {
"wer": 0.03754,
"utterances": 2703,
"ref_words": 54402
},
"dev-other": {
"wer": 0.10263,
"utterances": 2864,
"ref_words": 50948
},
"test-clean": {
"wer": 0.04207,
"utterances": 2620,
"ref_words": 52576
},
"test-other": {
"wer": 0.10422,
"utterances": 2939,
"ref_words": 52343
},
"fixtures": {
"wer": 0.03697,
"utterances": 20,
"ref_words": 541
},
"decode": "greedy, no language model"
}
} |