LGTM-TTS
LGTM (Looks Good To Me) is a text-to-speech model built by Claude Opus 5.5. Claude wrote the modeling code, collected and processed the training data, designed and ran the experiments, and trained and evaluated the model.
Multilingual text-to-speech at 44.1 kHz with 10 built-in voices and zero-shot voice cloning. Available in PyTorch and ONNX (ONNX Runtime needs no PyTorch).
Languages: English en, Spanish es, Portuguese pt, French fr, German de, Italian it,
Swedish sv, Vietnamese vi, Japanese ja, Korean ko, Indonesian id
Built-in voices: F1 F2 F3 F4 F5 (female), M1 M2 M3 M4 M5 (male)
Samples
All 10 built-in voices across the 11 languages (44.1 kHz).
| Language | Voice | Text | Audio |
|---|---|---|---|
| English | F1 | The quick brown fox jumps over the lazy dog, then naps in the warm afternoon sun. | |
| English | M2 | Could you remind me to call my sister tomorrow morning? I keep forgetting. | |
| Spanish | F2 | Hoy hace un día precioso. ¿Te apetece dar un paseo por el parque? | |
| Spanish | M1 | La biblioteca municipal abre a las nueve y cierra a las ocho de la tarde. | |
| Portuguese | F3 | Que bom te ver de novo! Vamos tomar um café e conversar um pouco? | |
| Portuguese | M3 | O comboio para Lisboa parte daqui a vinte minutos, não se atrase. | |
| French | F4 | Bonjour à tous, et bienvenue dans cette nouvelle émission consacrée à la science. | |
| French | M4 | Je pense qu'il va pleuvoir ce soir, n'oublie pas ton parapluie. | |
| German | F5 | Guten Morgen! Hast du gut geschlafen? Heute wird ein langer, aber schöner Tag. | |
| German | M5 | Die Bahn hat leider zwanzig Minuten Verspätung, wir sollten ein Taxi nehmen. | |
| Italian | F1 | Che bella giornata! Andiamo a prendere un gelato in piazza? | |
| Italian | M2 | Il museo resterà chiuso per lavori fino alla fine del mese prossimo. | |
| Swedish | F2 | Hej! Vill du följa med och fika på det nya kaféet vid torget? | |
| Swedish | M1 | Tåget mot Göteborg är tyvärr försenat på grund av ett signalfel. | |
| Vietnamese | F3 | Xin chào các bạn, hôm nay trời thật đẹp, chúng ta cùng đi dạo công viên nhé! | |
| Vietnamese | M3 | Cuốn sách này kể về hành trình của một cậu bé đi tìm ước mơ của mình. | |
| Japanese | F4 | こんにちは。今日はとても良い天気ですね。一緒に散歩に行きませんか？ | |
| Japanese | M4 | 駅までの道を教えていただけますか？初めてこの町に来ました。 | |
| Korean | F5 | 안녕하세요! 오늘 날씨가 정말 좋네요. 같이 산책하러 갈까요? | |
| Korean | M5 | 이번 주말에는 가족들과 함께 바닷가에 놀러 갈 예정이에요. | |
| Indonesian | F1 | Selamat pagi semuanya! Hari ini cuacanya cerah sekali, ayo kita jalan-jalan. | |
| Indonesian | M2 | Kereta menuju Bandung akan berangkat sepuluh menit lagi dari peron tiga. |
Setup
git clone https://huggingface.co/polyskill/LGTM
cd LGTM
pip install -r requirements.txt # PyTorch backend
pip install -r requirements-onnx.txt # ONNX backend
PyTorch
from lgtm import LGTMTTS
tts = LGTMTTS.from_pretrained(".") # or "polyskill/LGTM" to download
wav = tts.synthesize("Xin chào, hôm nay trời đẹp quá!", lang="vi", voice="F1")
tts.save_wav(wav, "out.wav") # 44.1 kHz mono
ONNX Runtime
from lgtm import LGTMOnnx
tts = LGTMOnnx.from_pretrained(".", use_gpu=False) # use_gpu=True with onnxruntime-gpu
wav = tts.synthesize("Bonjour à tous, comment allez-vous ?", lang="fr", voice="M1")
tts.save_wav(wav, "out.wav")
Voice cloning
Give 5-15 seconds of clean speech; the voice can then speak any supported language.
voice = tts.clone_voice("reference.wav") # works with both backends
wav = tts.synthesize("This is my cloned voice.", lang="en", voice=voice)
from lgtm import save_voice_style # ONNX: from lgtm.onnx_inference import save_voice_style
save_voice_style("my_voice.json", voice) # reuse later: voice="my_voice.json"
Command line
python -m lgtm.cli --text "Hej! Hur mår du idag?" --lang sv --voice F2 --out out.wav
python -m lgtm.cli --text "안녕하세요" --lang ko --ref reference.wav --save_voice my_voice.json --out out.wav
python -m lgtm.cli --text "Selamat pagi" --lang id --voice M3 --backend onnx --out out.wav
Options
| argument | default | |
|---|---|---|
lang | "en" | language code (see above) |
voice | "F1" | preset name, path to a voice .json, or clone_voice() output |
steps | 8 | denoising steps (fewer = faster, e.g. 5) |
speed | 1.05 | speaking rate (higher = faster) |
silence | 0.3 | seconds of silence between sentences (long text is split automatically) |
Files
| path | contents |
|---|---|
pytorch/model.safetensors | all weights (synthesis + voice cloning) |
onnx/text_encoder.onnx, duration_predictor.onnx, vector_estimator.onnx, vocoder.onnx | synthesis graphs |
onnx/voice_encoder.onnx | reference audio → voice style (cloning) |
voice_styles/*.json | built-in voices |
config.json, unicode_indexer.json | model config, text vocabulary |
lgtm/ | inference code (inference.py PyTorch, onnx_inference.py ONNX, cli.py) |
ONNX graph I/O (for custom runtimes)
| graph | inputs | outputs |
|---|---|---|
text_encoder | text_ids int64 [B,T], style_ttl [B,50,256], text_mask [B,1,T] | text_emb [B,256,T] |
duration_predictor | text_ids, style_dp [B,8,16], text_mask | duration [B] (seconds) |
vector_estimator | noisy_latent [B,144,L], text_emb, style_ttl, latent_mask [B,1,L], text_mask, current_step [B], total_step [B] | denoised_latent [B,144,L] |
vocoder | latent [B,144,L] | wav [B, 3072·L] |
voice_encoder | wav [1,N] (44.1 kHz) | style_ttl [1,50,256], style_dp [1,8,16] |
Sampling loop: L = ceil(duration·44100 / 3072), start from Gaussian noise masked by latent_mask,
call vector_estimator for current_step = 0 … total_step-1, then vocoder. See lgtm/onnx_inference.py.