Automatic Speech Recognition
LiteRT
English
embeddinggemma
speech-recognition
ctc
word-embeddings
on-device
core-ai
Instructions to use mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download reader_config.json from mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader: direct link, hf CLI and curl.
- Browser
- Download file 2.08 kB
-
https://huggingface.co/mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader/resolve/main/reader_config.json
- Command line
-
hf download hf://mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader/reader_config.json
-
curl -L -o reader_config.json https://huggingface.co/mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader/resolve/main/reader_config.json
2.08 kB
| { | |
| "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" | |
| } | |
| } |