model.py

import os
import json
from functools import lru_cache
from pathlib import Path

os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
os.environ.setdefault("HF_HUB_DISABLE_TELEMETRY", "1")

import sherpa_onnx
from huggingface_hub import hf_hub_download, snapshot_download


def get_file(repo_id: str, filename: str, subfolder: str = ".") -> str:
    return hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder)


@lru_cache(maxsize=10)
def _get_kokoro(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    data_dir = "/tmp/espeak-ng-data"
    clean_id = repo_id.split("|")[0]
    is_int8 = "int8" in clean_id
    model_name = "model.int8.onnx" if is_int8 else "model.onnx"
    model = get_file(repo_id=clean_id, filename=model_name, subfolder=".")
    tokens = get_file(repo_id=clean_id, filename="tokens.txt", subfolder=".")
    voices = get_file(repo_id=clean_id, filename="voices.bin", subfolder=".")
    if "multi-lang" in clean_id:
        lexicon_en = get_file(repo_id=clean_id, filename="lexicon-us-en.txt", subfolder=".")
        lexicon_zh = get_file(repo_id=clean_id, filename="lexicon-zh.txt", subfolder=".")
        lexicon = f"{lexicon_en},{lexicon_zh}"
        date_zh = get_file(repo_id=clean_id, filename="date-zh.fst", subfolder=".")
        number_zh = get_file(repo_id=clean_id, filename="number-zh.fst", subfolder=".")
        phone_zh = get_file(repo_id=clean_id, filename="phone-zh.fst", subfolder=".")
        rule_fsts = f"{date_zh},{phone_zh},{number_zh}"
        dict_dir = "/tmp/dict"
    else:
        lexicon = ""
        rule_fsts = ""
        dict_dir = ""
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            kokoro=sherpa_onnx.OfflineTtsKokoroModelConfig(
                model=model,
                voices=voices,
                tokens=tokens,
                data_dir=data_dir,
                length_scale=1.0 / speed,
                lexicon=lexicon,
                dict_dir=dict_dir,
            ),
            provider="cpu",
            debug=False,
            num_threads=2,
        ),
        max_num_sentences=1,
        rule_fsts=rule_fsts,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_supertonic(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    clean_id = repo_id.split("|")[0]
    dp = get_file(repo_id=clean_id, filename="duration_predictor.int8.onnx", subfolder=".")
    te = get_file(repo_id=clean_id, filename="text_encoder.int8.onnx", subfolder=".")
    ve = get_file(repo_id=clean_id, filename="vector_estimator.int8.onnx", subfolder=".")
    vo = get_file(repo_id=clean_id, filename="vocoder.int8.onnx", subfolder=".")
    tts_json = get_file(repo_id=clean_id, filename="tts.json", subfolder=".")
    ui = get_file(repo_id=clean_id, filename="unicode_indexer.bin", subfolder=".")
    vs = get_file(repo_id=clean_id, filename="voice.bin", subfolder=".")
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            supertonic=sherpa_onnx.OfflineTtsSupertonicModelConfig(
                duration_predictor=dp,
                text_encoder=te,
                vector_estimator=ve,
                vocoder=vo,
                tts_json=tts_json,
                unicode_indexer=ui,
                voice_style=vs,
            ),
            provider="cpu",
            debug=False,
            num_threads=2,
        ),
        max_num_sentences=1,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_vits_piper(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    data_dir = "/tmp/espeak-ng-data"
    lexicon = ""
    rule_fsts = ""
    clean_id = repo_id.split("|")[0]
    if "piper" in clean_id:
        n = len("vits-piper-")
        name = clean_id.split("/")[1][n:]
    elif "mimic3" in clean_id:
        n = len("vits-mimic3-")
        name = clean_id.split("/")[1][n:]
    else:
        name = "model"
    local_dir = snapshot_download(clean_id)
    model = f"{local_dir}/{name}.onnx"
    tokens = f"{local_dir}/tokens.txt"
    if "vits-piper-zh_CN-chaowen-medium" in clean_id or "vits-piper-zh_CN-xiao_ya-medium" in clean_id:
        data_dir = ""
        lexicon = f"{local_dir}/lexicon.txt"
        rule_fsts = ["phone.fst", "date.fst", "number.fst"]
        rule_fsts = ",".join(f"{local_dir}/{r}" for r in rule_fsts)
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(
                model=model,
                lexicon=lexicon,
                data_dir=data_dir,
                tokens=tokens,
                length_scale=1.0 / speed,
            ),
            matcha=sherpa_onnx.OfflineTtsMatchaModelConfig(),
            provider="cpu",
            debug=False,
            num_threads=2,
        ),
        max_num_sentences=1,
        rule_fsts=rule_fsts,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_vits_zh_aishell3(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    clean_id = repo_id.split("|")[0]
    model = get_file(repo_id=clean_id, filename="vits-aishell3.onnx", subfolder=".")
    lexicon = get_file(repo_id=clean_id, filename="lexicon.txt", subfolder=".")
    tokens = get_file(repo_id=clean_id, filename="tokens.txt", subfolder=".")
    rule_fsts = ["phone.fst", "date.fst", "number.fst", "new_heteronym.fst"]
    rule_fsts = ",".join(get_file(repo_id=clean_id, filename=f, subfolder=".") for f in rule_fsts)
    rule_fars = get_file(repo_id=clean_id, filename="rule.far", subfolder=".")
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(
                model=model, lexicon=lexicon, tokens=tokens, length_scale=1.0 / speed,
            ),
            matcha=sherpa_onnx.OfflineTtsMatchaModelConfig(),
            provider="cpu", debug=False, num_threads=2,
        ),
        rule_fsts=rule_fsts, rule_fars=rule_fars, max_num_sentences=1,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_matcha_zh_en(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    clean_id = repo_id.split("|")[0]
    acoustic_model = get_file(repo_id=clean_id, filename="model-steps-3.onnx", subfolder=".")
    vocoder = get_file(repo_id="csukuangfj/sherpa-onnx-vocoders", filename="vocos-16khz-univ.onnx", subfolder=".")
    lexicon = get_file(repo_id=clean_id, filename="lexicon.txt", subfolder=".")
    tokens = get_file(repo_id=clean_id, filename="tokens.txt", subfolder=".")
    rule_fsts = ["phone-zh.fst", "date-zh.fst", "number-zh.fst"]
    rule_fsts = ",".join(get_file(repo_id=clean_id, filename=f, subfolder=".") for f in rule_fsts)
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(),
            matcha=sherpa_onnx.OfflineTtsMatchaModelConfig(
                acoustic_model=acoustic_model, vocoder=vocoder,
                lexicon=lexicon, tokens=tokens, data_dir="/tmp/espeak-ng-data",
                length_scale=1.0 / speed,
            ),
            provider="cpu", debug=False, num_threads=2,
        ),
        rule_fsts=rule_fsts, rule_fars="", max_num_sentences=1,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_matcha_hf(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    clean_id = repo_id.split("|")[0]
    if not Path("/tmp/dict").is_dir():
        os.system("cd /tmp; curl -SL -O https://github.com/csukuangfj/cppjieba/releases/download/sherpa-onnx-2024-04-19/dict.tar.bz2; tar xf dict.tar.bz2")
    acoustic_model = get_file(repo_id=clean_id, filename="model-steps-3.onnx", subfolder=".")
    vocoder = get_file(repo_id="csukuangfj/sherpa-onnx-hifigan", filename="hifigan_v2.onnx", subfolder=".")
    lexicon = get_file(repo_id=clean_id, filename="lexicon.txt", subfolder=".")
    tokens = get_file(repo_id=clean_id, filename="tokens.txt", subfolder=".")
    rule_fsts = ["phone.fst", "date.fst", "number.fst"]
    rule_fsts = ",".join(get_file(repo_id=clean_id, filename=f, subfolder=".") for f in rule_fsts)
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(),
            matcha=sherpa_onnx.OfflineTtsMatchaModelConfig(
                acoustic_model=acoustic_model, vocoder=vocoder,
                lexicon=lexicon, tokens=tokens, dict_dir="/tmp/dict",
                length_scale=1.0 / speed,
            ),
            provider="cpu", debug=False, num_threads=2,
        ),
        rule_fsts=rule_fsts, rule_fars="", max_num_sentences=1,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_vits_hf(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    clean_id = repo_id.split("|")[0]
    if "fanchen" in clean_id or "vits-cantonese-hf-xiaomaiiwn" in clean_id:
        model = clean_id.split("/")[-1]
    elif "vits-melo-tts" in clean_id:
        model = "model"
    else:
        model = "model"
    if "vits-zh-ll" in clean_id or "vits-melo-tts" in clean_id:
        if not Path("/tmp/dict").is_dir():
            os.system("cd /tmp; curl -SL -O https://github.com/csukuangfj/cppjieba/releases/download/sherpa-onnx-2024-04-19/dict.tar.bz2; tar xf dict.tar.bz2")
    model_file = get_file(repo_id=clean_id, filename=f"{model}.onnx", subfolder=".")
    lexicon = get_file(repo_id=clean_id, filename="lexicon.txt", subfolder=".")
    tokens = get_file(repo_id=clean_id, filename="tokens.txt", subfolder=".")
    if "vits-cantonese-hf-xiaomaiiwn" not in clean_id:
        rule_fsts = ["phone.fst", "date.fst", "number.fst"]
        rule_fsts = ",".join(get_file(repo_id=clean_id, filename=f, subfolder=".") for f in rule_fsts)
        vits_dict_dir = "/tmp/dict"
    else:
        rule_fsts = get_file(repo_id=clean_id, filename="rule.fst", subfolder=".")
        vits_dict_dir = ""
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(
                model=model_file, lexicon=lexicon, tokens=tokens,
                dict_dir=vits_dict_dir, length_scale=1.0 / speed,
            ),
            matcha=sherpa_onnx.OfflineTtsMatchaModelConfig(),
            provider="cpu", debug=False, num_threads=2,
        ),
        rule_fsts=rule_fsts, rule_fars="", max_num_sentences=1,
    )
    return sherpa_onnx.OfflineTts(tts_config)


@lru_cache(maxsize=10)
def _get_melotts_onnx(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    clean_id = repo_id.split("|")[0]
    parts = clean_id.split("/")
    lang_subdir = parts[2] if len(parts) > 2 else parts[1]
    model = hf_hub_download(repo_id="MiaoMint/MeloTTS-ONNX", filename=f"onnx_exports/{lang_subdir}/model.onnx")
    tokens = hf_hub_download(repo_id="MiaoMint/MeloTTS-ONNX", filename=f"onnx_exports/{lang_subdir}/tokens.txt")
    lexicon_path = ""
    try:
        lexicon_path = hf_hub_download(repo_id="MiaoMint/MeloTTS-ONNX", filename=f"onnx_exports/{lang_subdir}/lexicon.txt")
    except Exception:
        pass
    data_dir = "/tmp/espeak-ng-data"
    dict_dir = "/tmp/dict" if lang_subdir == "zh" else ""
    if dict_dir and not Path(dict_dir).is_dir():
        os.system("cd /tmp; curl -SL -O https://github.com/csukuangfj/cppjieba/releases/download/sherpa-onnx-2024-04-19/dict.tar.bz2; tar xf dict.tar.bz2")
    tts_config = sherpa_onnx.OfflineTtsConfig(
        model=sherpa_onnx.OfflineTtsModelConfig(
            vits=sherpa_onnx.OfflineTtsVitsModelConfig(
                model=model, lexicon=lexicon_path, data_dir=data_dir,
                tokens=tokens, dict_dir=dict_dir, length_scale=1.0 / speed,
            ),
            matcha=sherpa_onnx.OfflineTtsMatchaModelConfig(),
            provider="cpu", debug=False, num_threads=2,
        ),
        max_num_sentences=1,
    )
    return sherpa_onnx.OfflineTts(tts_config)


chinese_models = {
    "csukuangfj2/vits-piper-zh_CN-chaowen-medium|1 speaker": _get_vits_piper,
    "csukuangfj2/vits-piper-zh_CN-xiao_ya-medium|1 speaker": _get_vits_piper,
    "csukuangfj/matcha-icefall-zh-baker|1 speaker": _get_matcha_hf,
    "csukuangfj/vits-zh-hf-fanchen-wnj|1 speaker": _get_vits_hf,
    "csukuangfj/vits-zh-hf-fanchen-C|187 speakers": _get_vits_hf,
    "csukuangfj/sherpa-onnx-vits-zh-ll|5 speakers": _get_vits_hf,
    "csukuangfj/vits-zh-aishell3|174 speakers": _get_vits_zh_aishell3,
    "csukuangfj/vits-piper-zh_CN-huayan-medium|1 speaker": _get_vits_piper,
    "MiaoMint/MeloTTS-ONNX/zh|1 speaker": _get_melotts_onnx,
}

chinese_english_models = {
    "csukuangfj/matcha-icefall-zh-en|1": _get_matcha_zh_en,
    "csukuangfj/kokoro-multi-lang-v1_1|103 speakers": _get_kokoro,
    "csukuangfj/kokoro-int8-multi-lang-v1_1|103 speakers": _get_kokoro,
    "csukuangfj/kokoro-multi-lang-v1_0|53 speakers": _get_kokoro,
    "csukuangfj/kokoro-int8-multi-lang-v1_0|53 speakers": _get_kokoro,
    "csukuangfj2/sherpa-onnx-supertonic-3-tts-int8-2026-05-11|10 speakers": _get_supertonic,
    "csukuangfj/vits-melo-tts-zh_en|1": _get_vits_hf,
    "MiaoMint/MeloTTS-ONNX/zh|1 speaker": _get_melotts_onnx,
}

cantonese_models = {
    "csukuangfj/vits-cantonese-hf-xiaomaiiwn|1 speaker": _get_vits_hf,
}

english_models = {
    "csukuangfj/kokoro-en-v0_19|11 speakers": _get_kokoro,
    "csukuangfj2/sherpa-onnx-supertonic-3-tts-int8-2026-05-11|10 speakers": _get_supertonic,
    "MiaoMint/MeloTTS-ONNX/en_newest|1 speaker": _get_melotts_onnx,
    "csukuangfj/vits-piper-en_US-lessac-high|1 speaker": _get_vits_piper,
    "csukuangfj/vits-piper-en_US-ryan-high|1 speaker": _get_vits_piper,
    "csukuangfj/vits-piper-en_GB-alan-medium|1 speaker": _get_vits_piper,
    "csukuangfj/vits-piper-en_GB-jenny_dioco-medium|1 speaker": _get_vits_piper,
    "csukuangfj/vits-piper-en_GB-vctk-medium|109 speakers": _get_vits_piper,
}

language_to_models = {
    "中文（普通话）": list(chinese_models.keys()),
    "中英双语": list(chinese_english_models.keys()),
    "粤语": list(cantonese_models.keys()),
    "英语": list(english_models.keys()),
}

all_model_dicts = {
    **chinese_models,
    **chinese_english_models,
    **cantonese_models,
    **english_models,
}


@lru_cache(maxsize=32)
def get_speaker_map(repo_id: str) -> dict:
    clean_id = repo_id.split("|")[0]
    if "piper" in clean_id:
        try:
            local_dir = snapshot_download(clean_id)
            for fn in os.listdir(local_dir):
                if fn.endswith(".onnx.json"):
                    with open(os.path.join(local_dir, fn)) as f:
                        data = json.load(f)
                    sid_map = data.get("speaker_id_map", {})
                    return {v: k for k, v in sid_map.items()}
        except Exception:
            pass
    if "vits-zh-hf-fanchen-C" in clean_id:
        try:
            f = hf_hub_download(repo_id=clean_id, filename="G_C.json")
            with open(f) as fp:
                data = json.load(fp)
            speakers = data.get("speakers", [])
            return {i: s for i, s in enumerate(speakers)}
        except Exception:
            pass
    if "vits-zh-aishell3" in clean_id:
        return {}
    if "kokoro" in clean_id:
        kokoro_en = [
            (0, "美式女声-af"), (1, "美式女声-bella"),
            (2, "美式女声-nicole"), (3, "美式女声-sarah"),
            (4, "美式女声-sky"), (5, "美式男声-adam"),
            (6, "美式男声-michael"), (7, "英式女声-emma"),
            (8, "英式女声-isabella"), (9, "英式男声-george"),
            (10, "英式男声-lewis"),
        ]
        kokoro_multi_v10 = [
            (0, "美式女声-alloy"), (1, "美式女声-aoede"),
            (2, "美式女声-bella"), (3, "美式女声-heart"),
            (4, "美式女声-jessica"), (5, "美式女声-kore"),
            (6, "美式女声-nicole"), (7, "美式女声-nova"),
            (8, "美式女声-river"), (9, "美式女声-sarah"),
            (10, "美式女声-sky"), (11, "美式男声-adam"),
            (12, "美式男声-echo"), (13, "美式男声-eric"),
            (14, "美式男声-fenrir"), (15, "美式男声-liam"),
            (16, "美式男声-michael"), (17, "美式男声-onyx"),
            (18, "美式男声-puck"), (19, "美式男声-santa"),
            (20, "英式女声-alice"), (21, "英式女声-emma"),
            (22, "英式女声-isabella"), (23, "英式女声-lily"),
            (24, "英式男声-daniel"), (25, "英式男声-fable"),
            (26, "英式男声-george"), (27, "英式男声-lewis"),
            (28, "英语女声-dora"), (29, "英语男声-alex"),
            (30, "法语女声-siwis"), (31, "印地语女声-alpha"),
            (32, "印地语女声-beta"), (33, "印地语男声-omega"),
            (34, "印地语男声-psi"), (35, "意语女声-sara"),
            (36, "意语男声-nicola"), (37, "日语女声-alpha"),
            (38, "日语女声-gongitsune"), (39, "日语女声-nezumi"),
            (40, "日语女声-tebukuro"), (41, "日语男声-kumo"),
            (42, "葡语女声-dora"), (43, "葡语男声-alex"),
            (44, "葡语男声-santa"), (45, "中文女声-小北"),
            (46, "中文女声-小妮"), (47, "中文女声-小小"),
            (48, "中文女声-小艺"), (49, "中文男声-云剑"),
            (50, "中文男声-云希"), (51, "中文男声-云夏"),
            (52, "中文男声-云扬"),
        ]
        kokoro_multi_v11 = [
            (0, "美式女声-maple"), (1, "美式女声-sol"),
            (2, "英式女声-vale"),
            (3, "中文女声-001"), (4, "中文女声-002"),
            (5, "中文女声-003"), (6, "中文女声-004"),
            (7, "中文女声-005"), (8, "中文女声-006"),
            (9, "中文女声-007"), (10, "中文女声-008"),
            (11, "中文女声-017"), (12, "中文女声-018"),
            (13, "中文女声-019"), (14, "中文女声-021"),
            (15, "中文女声-022"), (16, "中文女声-023"),
            (17, "中文女声-024"), (18, "中文女声-026"),
            (19, "中文女声-027"), (20, "中文女声-028"),
            (21, "中文女声-032"), (22, "中文女声-036"),
            (23, "中文女声-038"), (24, "中文女声-039"),
            (25, "中文女声-040"), (26, "中文女声-042"),
            (27, "中文女声-043"), (28, "中文女声-044"),
            (29, "中文女声-046"), (30, "中文女声-047"),
            (31, "中文女声-048"), (32, "中文女声-049"),
            (33, "中文女声-051"), (34, "中文女声-059"),
            (35, "中文女声-060"), (36, "中文女声-067"),
            (37, "中文女声-070"), (38, "中文女声-071"),
            (39, "中文女声-072"), (40, "中文女声-073"),
            (41, "中文女声-074"), (42, "中文女声-075"),
            (43, "中文女声-076"), (44, "中文女声-077"),
            (45, "中文女声-078"), (46, "中文女声-079"),
            (47, "中文女声-083"), (48, "中文女声-084"),
            (49, "中文女声-085"), (50, "中文女声-086"),
            (51, "中文女声-087"), (52, "中文女声-088"),
            (53, "中文女声-090"), (54, "中文女声-092"),
            (55, "中文女声-093"), (56, "中文女声-094"),
            (57, "中文女声-099"),
            (58, "中文男声-009"), (59, "中文男声-010"),
            (60, "中文男声-011"), (61, "中文男声-012"),
            (62, "中文男声-013"), (63, "中文男声-014"),
            (64, "中文男声-015"), (65, "中文男声-016"),
            (66, "中文男声-020"), (67, "中文男声-025"),
            (68, "中文男声-029"), (69, "中文男声-030"),
            (70, "中文男声-031"), (71, "中文男声-033"),
            (72, "中文男声-034"), (73, "中文男声-035"),
            (74, "中文男声-037"), (75, "中文男声-041"),
            (76, "中文男声-045"), (77, "中文男声-050"),
            (78, "中文男声-052"), (79, "中文男声-053"),
            (80, "中文男声-054"), (81, "中文男声-055"),
            (82, "中文男声-056"), (83, "中文男声-057"),
            (84, "中文男声-058"), (85, "中文男声-061"),
            (86, "中文男声-062"), (87, "中文男声-063"),
            (88, "中文男声-064"), (89, "中文男声-065"),
            (90, "中文男声-066"), (91, "中文男声-068"),
            (92, "中文男声-069"), (93, "中文男声-080"),
            (94, "中文男声-081"), (95, "中文男声-082"),
            (96, "中文男声-089"), (97, "中文男声-091"),
            (98, "中文男声-095"), (99, "中文男声-096"),
            (100, "中文男声-097"), (101, "中文男声-098"),
            (102, "中文男声-100"),
        ]
        kokoro_maps = {
            "csukuangfj/kokoro-en-v0_19": kokoro_en,
            "csukuangfj/kokoro-multi-lang-v1_1": kokoro_multi_v11,
            "csukuangfj/kokoro-multi-lang-v1_0": kokoro_multi_v10,
            "csukuangfj/kokoro-int8-multi-lang-v1_1": kokoro_multi_v11,
            "csukuangfj/kokoro-int8-multi-lang-v1_0": kokoro_multi_v10,
        }
        if clean_id in kokoro_maps:
            return {sid: name for sid, name in kokoro_maps[clean_id]}
    if "supertonic" in clean_id:
        return {
            0: "男声-M1（活泼自信）",
            1: "男声-M2（深沉稳重）",
            2: "男声-M3（专业权威）",
            3: "男声-M4（柔和亲切）",
            4: "男声-M5（温暖舒缓）",
            5: "女声-F1（沉稳从容）",
            6: "女声-F2（明快活泼）",
            7: "女声-F3（清晰专业）",
            8: "女声-F4（干练自信）",
            9: "女声-F5（温柔平和）",
        }
    if "vits-zh-ll" in clean_id:
        return {0: "女声0", 1: "女声1", 2: "男声0", 3: "男声1", 4: "男声2"}
    return {}


@lru_cache(maxsize=10)
def get_pretrained_model(repo_id: str, speed: float) -> sherpa_onnx.OfflineTts:
    if repo_id in all_model_dicts:
        return all_model_dicts[repo_id](repo_id, speed)
    raise ValueError(f"不支持的模型: {repo_id}")