androiddrew/g2p-en-decoder-onnx

g2p_en decoder: keyword text features, ONNX

The g2p_en grapheme-to-phoneme model (Kyubyong Park & Jongseok Kim, Apache-2.0, release 2.1.0) as two ONNX graphs, converted from androiddrew/g2p-en-decoder-pytorch. The values are unchanged.

It is not used to output phoneme strings. The keyword model androiddrew/naptrap-phonmatchnet-en-onnx was trained on the decoder's hidden states. It is embedded in go-phonmatchnet.

Not affiliated with or endorsed by the g2p_en authors. See LICENSE and NOTICE.

Files

Graph	Inputs	Outputs
encoder.onnx	graphemes int64 [L]: a word's grapheme IDs, then </s> (2)	hidden [1, 256]; separator [1, 256], the row placed between words
decoder.onnx	token int64 [1], hidden [1, 256]	next_hidden [1, 256]; logits [1, 74]

config.json lists the grapheme vocabulary, start/end phoneme IDs, the 20-step limit and the SHA-256 of both graphs. conversion/ holds the export script, its locked environment and README.

Keyword features

1. Normalize the keyword: NFKD, drop combining marks, lowercase, and keep words matching [a-z]+(?:'[a-z]+)*. Numbers must be spelled out.
2. Encode each word: grapheme IDs from config.json (<unk> for '), then </s>.
3. Decode greedily from token 2 (<s>) for up to 20 steps; each step's next_hidden is one feature row, until the argmax token is 3 (</s>), whose state is not kept.
4. Put the separator row between words.

import numpy as np
import onnxruntime as ort

encoder = ort.InferenceSession("encoder.onnx")
decoder = ort.InferenceSession("decoder.onnx")
graphemes = {g: i for i, g in enumerate(config["graphemes"])}

def features(normalized):
    rows = []
    for i, word in enumerate(normalized.split()):
        ids = np.array([graphemes.get(c, 1) for c in word] + [2], dtype=np.int64)
        hidden, separator = encoder.run(None, {"graphemes": ids})
        if i:
            rows.append(separator)
        token = 2
        for _ in range(20):
            hidden, logits = decoder.run(None, {"token": np.array([token]), "hidden": hidden})
            token = int(logits.argmax())
            if token == 3:
                break
            rows.append(hidden)
    return np.concatenate(rows)   # float32 [positions, 256]

Conversion and accuracy

conversion/export.py loads the PyTorch release at commit fc55559c25a12fce6e0cf9062729e7f2a9fc1ddc with naptrap 0.1.0. The grapheme GRU has PyTorch's gate layout, so the encoder is an ONNX GRU operator; the decoder is one GRU-cell step plus the output projection. Over eight keywords, including apostrophes, accents, one letter, a 34-letter word and a nine-word sentence, every feature length matched naptrap and the maximum difference was 5.5e-6. With the locked environment it reproduces both graphs byte for byte.

Limitations

The model was trained on CMUdict pronunciations of American English words. Unusual names, brand names and non-English words get its best-guess pronunciation, which may not match how people say them. The dictionary-lookup, homograph and number-expansion steps of the g2p_en package are not used.