Export https://github.com/KittenML/KittenTTS to sherpa-onnx (#2456)

+name: export-kitten-to-onnx
+
+on:
+  push:
+    branches:
+      - kitten-tts
+
+  workflow_dispatch:
+
+concurrency:
+  group: export-kitten-to-onnx-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  export-kitten-to-onnx:
+    if: github.repository_owner == 'k2-fsa' || github.repository_owner == 'csukuangfj'
+    name: export kitten ${{ matrix.version }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.10"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Python dependencies
+        shell: bash
+        run: |
+          pip install "numpy<=1.26.4" onnx==1.16.0 onnxruntime==1.17.1 librosa soundfile piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html
+
+      - name: Run
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        shell: bash
+        run: |
+          cd scripts/kitten-tts/nano_v0_1
+          ./run.sh
+
+      - name: Collect results
+        shell: bash
+        run: |
+          curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
+          tar xf espeak-ng-data.tar.bz2
+          rm espeak-ng-data.tar.bz2
+
+          src=scripts/kitten-tts/nano_v0_1
+
+          d=kitten-nano-en-v0_1-fp16
+
+          mkdir $d
+          cp -a LICENSE $d/LICENSE
+          cp -a espeak-ng-data $d/
+          cp -v $src/model.fp16.onnx $d/model.fp16.onnx
+          cp -v $src/voices.bin $d/
+          cp -v $src/tokens.txt $d/
+          cp -v $src/../README.md $d/README.md
+          ls -lh $d/
+          tar cjfv $d.tar.bz2 $d
+
+          ls -lh $d.tar.bz2
+
+      - name: Release
+        if: github.repository_owner == 'csukuangfj'
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          file: ./*.tar.bz2
+          overwrite: true
+          repo_name: k2-fsa/sherpa-onnx
+          repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
+          tag: tts-models
+
+      - name: Release
+        if: github.repository_owner == 'k2-fsa'
+        uses: svenstaro/upload-release-action@v2
+        with:
+          file_glob: true
+          file: ./*.tar.bz2
+          overwrite: true
+          tag: tts-models
+
+      - name: Publish to huggingface
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v3
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            dirs=(
+              kitten-nano-en-v0_1-fp16
+            )
+
+            export GIT_LFS_SKIP_SMUDGE=1
+            export GIT_CLONE_PROTECTION_ACTIVE=false
+
+            for d in ${dirs[@]}; do
+              rm -rf huggingface
+
+              git clone https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/$d huggingface
+              cd huggingface
+              rm -rf ./*
+
+              git lfs track "*.onnx"
+              git lfs track af_dict
+              git lfs track ar_dict
+              git lfs track cmn_dict
+              git lfs track da_dict en_dict fa_dict hu_dict ia_dict it_dict lb_dict phondata ru_dict ta_dict
+              git lfs track ur_dict yue_dict
+
+              cp -a ../$d/* ./
+
+              git add .
+
+              ls -lh
+
+              git status
+
+              git commit -m "add models"
+              git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/$d main || true
+            done

@@ -142,3 +142,5 @@ README-DEV.txt
 .idea
 sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02
 dict
+*.npz
+voices.bin

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+
+import argparse
+
+import numpy as np
+import onnx
+
+from generate_voices_bin import speaker2id
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", type=str, required=True, help="input and output onnx model"
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    print(args.model)
+
+    model = onnx.load(args.model)
+
+    style = np.load("./voices.npz")
+    style_shape = style[list(style.keys())[0]].shape
+
+    speaker2id_str = ""
+    id2speaker_str = ""
+    sep = ""
+    for s, i in speaker2id.items():
+        speaker2id_str += f"{sep}{s}->{i}"
+        id2speaker_str += f"{sep}{i}->{s}"
+        sep = ","
+
+    meta_data = {
+        "model_type": "kitten-tts",
+        "language": "English",
+        "has_espeak": 1,
+        "sample_rate": 24000,
+        "version": 1,
+        "voice": "en-us",
+        "style_dim": ",".join(map(str, style_shape)),
+        "n_speakers": len(speaker2id),
+        "speaker2id": speaker2id_str,
+        "id2speaker": id2speaker_str,
+        "speaker_names": ",".join(map(str, speaker2id.keys())),
+        "model_url": "https://huggingface.co/KittenML/kitten-tts-nano-0.1",
+        "see_also": "https://github.com/KittenML/KittenTTS",
+        "maintainer": "k2-fsa",
+        "comment": "This is kitten-tts-nano-0.1 and supports only English",
+    }
+
+    print(model.metadata_props)
+
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = str(value)
+    print("--------------------")
+
+    print(model.metadata_props)
+
+    onnx.save(model, args.model)
+
+    print(f"Please see {args.model}")
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+Change the model so that it can be run in onnxruntime 1.17.1
+"""
+
+import onnx
+
+
+def main():
+    model = onnx.load("kitten_tts_nano_v0_1.onnx")
+
+    # Print current opsets
+    for opset in model.opset_import:
+        print(f"Domain: '{opset.domain}', Version: {opset.version}")
+
+    # Modify the opset versions (be careful!)
+    for opset in model.opset_import:
+        if opset.domain == "":  # ai.onnx domain
+            opset.version = 19  # change from 20 to 19
+        elif opset.domain == "ai.onnx.ml":
+            opset.version = 4  # change from 5 to 4
+
+    # Save the modified model
+    onnx.save(model, "model.fp16.onnx")
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+
+def get_vocab():
+    # https://github.com/KittenML/KittenTTS/blob/main/kittentts/onnx_model.py#L17
+    _pad = "$"
+    _punctuation = ';:,.!?¡¿—…"«»"" '
+    _letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
+    _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
+
+    symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
+    dicts = {}
+    for i in range(len((symbols))):
+        dicts[symbols[i]] = i
+    return dicts
+
+
+def main():
+    token2id = get_vocab()
+    with open("tokens.txt", "w", encoding="utf-8") as f:
+        for s, i in token2id.items():
+            f.write(f"{s} {i}\n")
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+from pathlib import Path
+
+import numpy as np
+
+speakers = [
+    "expr-voice-2-m",
+    "expr-voice-2-f",
+    "expr-voice-3-m",
+    "expr-voice-3-f",
+    "expr-voice-4-m",
+    "expr-voice-4-f",
+    "expr-voice-5-m",
+    "expr-voice-5-f",
+]
+
+id2speaker = {idx: speaker for idx, speaker in enumerate(speakers)}
+
+speaker2id = {speaker: idx for idx, speaker in id2speaker.items()}
+
+
+def main():
+    if Path("./voices.bin").is_file():
+        print("./voices.bin exists - skip")
+        return
+
+    voices = np.load("./voices.npz")
+
+    with open("voices.bin", "wb") as f:
+        for speaker in speakers:
+            v = voices[speaker]
+            # v.shape (1, 256)
+            f.write(v.tobytes())
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env bash
+# Copyright      2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+set -ex
+
+if [ ! -f kitten_tts_nano_v0_1.onnx ]; then
+  curl -SL -O https://huggingface.co/KittenML/kitten-tts-nano-0.1/resolve/main/kitten_tts_nano_v0_1.onnx
+fi
+
+if [ ! -f voices.npz ]; then
+  curl -SL -O https://huggingface.co/KittenML/kitten-tts-nano-0.1/resolve/main/voices.npz
+fi
+
+./generate_voices_bin.py
+./generate_tokens.py
+./convert_opset.py
+./show.py
+./add_meta_data.py --model ./model.fp16.onnx
+# ./test.py --model ./model.fp16.onnx --tokens ./tokens.txt --voice ./voices.bin
+ls -lh

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import onnxruntime
+import onnx
+
+"""
+[key: "onnx.infer"
+value: "onnxruntime.quant"
+, key: "onnx.quant.pre_process"
+value: "onnxruntime.quant"
+]
+NodeArg(name='input_ids', type='tensor(int64)', shape=[1, 'sequence_length'])
+NodeArg(name='style', type='tensor(float)', shape=[1, 256])
+NodeArg(name='speed', type='tensor(float)', shape=[1])
+-----
+NodeArg(name='waveform', type='tensor(float)', shape=['num_samples'])
+NodeArg(name='duration', type='tensor(int64)', shape=['Castduration_dim_0'])
+"""
+
+
+def show(filename):
+    model = onnx.load(filename)
+    print(model.metadata_props)
+
+    session_opts = onnxruntime.SessionOptions()
+    session_opts.log_severity_level = 3
+    sess = onnxruntime.InferenceSession(
+        filename, session_opts, providers=["CPUExecutionProvider"]
+    )
+    for i in sess.get_inputs():
+        print(i)
+
+    print("-----")
+
+    for i in sess.get_outputs():
+        print(i)
+
+
+def main():
+    show("./model.fp16.onnx")
+
+
+if __name__ == "__main__":
+    main()

+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import argparse
+import time
+from pathlib import Path
+from typing import Dict, List
+
+import numpy as np
+
+try:
+    from piper_phonemize import phonemize_espeak
+except Exception as ex:
+    raise RuntimeError(
+        f"{ex}\nPlease run\n"
+        "pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html"
+    )
+
+import onnxruntime as ort
+import soundfile as sf
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help="Path to the model",
+    )
+
+    parser.add_argument(
+        "--voices-bin",
+        type=str,
+        required=True,
+        help="Path to the voices.bin",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        required=True,
+        help="Path to tokens.txt",
+    )
+    return parser.parse_args()
+
+
+def show(filename):
+    session_opts = ort.SessionOptions()
+    session_opts.log_severity_level = 3
+    sess = ort.InferenceSession(filename, session_opts)
+    for i in sess.get_inputs():
+        print(i)
+
+    print("-----")
+
+    for i in sess.get_outputs():
+        print(i)
+
+
+def load_tokens(filename: str) -> Dict[str, int]:
+    ans = dict()
+    with open(filename, encoding="utf-8") as f:
+        for line in f:
+            fields = line.strip().split()
+            if len(fields) == 2:
+                token, idx = fields
+                ans[token] = int(idx)
+            else:
+                assert len(fields) == 1, (len(fields), line)
+                ans[" "] = int(fields[0])
+    return ans
+
+
+def load_voices(speaker_names: List[str], dim: List[int], voices_bin: str):
+    embedding = (
+        np.fromfile(voices_bin, dtype="uint8")
+        .view(np.float32)
+        .reshape(len(speaker_names), *dim)
+    )
+    ans = dict()
+    for i in range(len(speaker_names)):
+        ans[speaker_names[i]] = embedding[i]
+
+    return ans
+
+
+class OnnxModel:
+    def __init__(self, model_filename: str, voices_bin: str, tokens: str):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.session_opts = session_opts
+        self.model = ort.InferenceSession(
+            model_filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+        self.token2id = load_tokens(tokens)
+
+        meta = self.model.get_modelmeta().custom_metadata_map
+        print(meta)
+        dim = list(map(int, meta["style_dim"].split(",")))
+        speaker_names = meta["speaker_names"].split(",")
+
+        self.voices = load_voices(
+            speaker_names=speaker_names, dim=dim, voices_bin=voices_bin
+        )
+
+        self.sample_rate = int(meta["sample_rate"])
+
+    def __call__(self, text: str, voice):
+        tokens = phonemize_espeak(text, "en-us")
+        # tokens is List[List[str]]
+        # Each sentence is a List[str]
+        # len(tokens) == number of sentences
+
+        flatten = []
+        for t in tokens:
+            flatten.extend(t)
+            # we append a space at the end of a sentence so that there is
+            # a pause in the generated audio
+            flatten.append(" ")
+
+        tokens = "".join(flatten)
+
+        tokens = list(tokens)
+
+        token_ids = [self.token2id[i] for i in tokens]
+
+        style = self.voices[voice]
+
+        token_ids = [0, *token_ids, 0]
+        token_ids = np.array([token_ids], dtype=np.int64)
+
+        speed = np.array([1.0], dtype=np.float32)
+
+        audio = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: token_ids,
+                self.model.get_inputs()[1].name: style,
+                self.model.get_inputs()[2].name: speed,
+            },
+        )[0]
+        return audio
+
+
+def main():
+    args = get_args()
+    print(vars(args))
+    show(args.model)
+
+    #  tokens = phonemize_espeak("how are you doing?", "en-us")
+    # [['h', 'ˌ', 'a', 'ʊ', ' ', 'ɑ', 'ː', 'ɹ', ' ', 'j', 'u', 'ː', ' ', 'd', 'ˈ', 'u', 'ː', 'ɪ', 'ŋ', '?']]
+    m = OnnxModel(
+        model_filename=args.model, voices_bin=args.voices_bin, tokens=args.tokens
+    )
+
+    text = (
+        "Today as always, men fall into two groups: slaves and free men. "
+        + " Whoever does not have two-thirds of his day for himself, "
+        + "is a slave, whatever he may be: a statesman, a businessman, "
+        + "an official, or a scholar."
+    )
+
+    for i, voice in enumerate(m.voices.keys(), 1):
+        print(f"Testing {i}/{len(m.voices)} - {voice}/{args.model}")
+
+        start = time.time()
+        audio = m(text, voice=voice)
+        end = time.time()
+
+        elapsed_seconds = end - start
+        audio_duration = len(audio) / m.sample_rate
+        real_time_factor = elapsed_seconds / audio_duration
+
+        filename = f"{Path(args.model).stem}-{voice}.wav"
+        sf.write(
+            filename,
+            audio,
+            samplerate=m.sample_rate,
+            subtype="PCM_16",
+        )
+        print(f" Saved to {filename}")
+        print(f" Elapsed seconds: {elapsed_seconds:.3f}")
+        print(f" Audio duration in seconds: {audio_duration:.3f}")
+        print(
+            f" RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}"
+        )
+
+
+if __name__ == "__main__":
+    main()