myned-ai / pocket-tts-greek Like 2 Follow Myned AI 7

Pocket TTS — Greek (6 layers)

Greek and Cypriot Greek text-to-speech for Pocket TTS, running faster than real time on a CPU.

• Trained on \~2,400 hours of Greek speech from 3,900+ speakers, balanced between female and male voices.
• As intelligible as real speech: Whisper transcribes it as accurately as real recordings of the same sentences.
• Voice cloning from a 10–15 s sample, plus eight curated voices, two of them Cypriot.
• \~110 M parameters, 3× faster than real time on a laptop CPU, with streaming output.
• Own Greek tokenizer, trained on correctly punctuated Greek text.

Voices

Every clip below was generated by this model, not recorded.

| voice | description | sample | | ------------ | ----------------------------------------------- | ------ | | Eleni | female, young adult, calm and warm | | | Athina | female, young adult, clear and professional | | | Katerina | female, adult, upbeat and expressive | | | Sophia | female, young adult, Cypriot, bright and lively | | | Spyros | male, adult, deep, calm and warm | | | Kostas | male, young adult, clear and professional | | | Nikos | male, adult, deep, upbeat and friendly | | | Giorgos | male, mature, Cypriot, expressive | |

Use a voice by passing its file, e.g. `--voice hf://myned-ai/pocket-tts-greek/voices/eleni.wav`. Descriptions are approximate. `voices/voices.md` lists the source recording and the cleaning applied to each voice. The named English voices (`alba`, …) do not work with this model; any other clean recording of a speaker does.

Usage

uvx pocket-tts generate \
  --config hf://myned-ai/pocket-tts-greek/greek.yaml \
  --voice hf://myned-ai/pocket-tts-greek/voices/kostas.wav \
  --text "Καλημέρα σας! Θέλετε να κλείσουμε ένα ραντεβού για αύριο;"

from pocket_tts import TTSModel
model = TTSModel.load_model(config="hf://myned-ai/pocket-tts-greek/greek.yaml")
voice = model.get_state_for_audio_prompt("hf://myned-ai/pocket-tts-greek/voices/eleni.wav")
audio = model.generate_audio(voice, "Γεια σας, πώς μπορώ να σας βοηθήσω;")

• Questions end with `;`, the Greek question mark. The config keeps it; do not convert it.
• Write numbers as words for best results ("στις δέκα και μισή", not "στις 10:30").
• Default temperature is 0.3; 0.5–0.7 gives livelier but less even delivery.
• For your own voice: 10–15 s of one continuous, clean take, ideally with the speaking style you want. The model copies the recording's quality, room and pace.

Training

| | | | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | data | 2,395 h of Greek speech, 766k utterances, 3,900+ speakers, 50 % female; 115 h Cypriot. Mostly Greek podcasts (automatically transcribed and aligned) plus Mozilla Common Voice (el, el-CY) | | tokenizer | SentencePiece, 4,000 tokens, trained on the transcripts | | teacher | 24 layers, fine-tuned from english\_2026-04\_24l with a fresh text embedding, 300k steps | | student | 6 layers, depth-distilled from the teacher (guidance baked in), 250k steps | | punctuation | The ASR marked questions with the Latin ? instead of the Greek question mark ;. We converted them to the correct Greek form before training. | | hardware | one GH200, \~23 h in total |

Trained with the Pocket TTS training code.

Evaluation

80 Greek sentences from 10 Common Voice speakers never seen in training. The voice prompt is a different recording of the same speaker. Whisper large-v3 is the judge, at temperature 0.3.

| | this model | real recordings | | -------------------------- | ------------------------------------------- | ---------------------- | | word error rate | 11.2 % (one Whisper hallucination excluded) | 12.5 % | | speaker similarity (WavLM) | 0.912 | 0.937 (two real clips) | | UTMOS | 2.98 | 2.46 |

Whisper misreads 12.5 % of words even in real Greek speech, and the test sentences are largely literary, so absolute error rates here are not comparable with English models.

Across the 8 voices × 59 agent-style sentences, 87 % of clips were read cleanly.

Limitations

• Very short replies ("Εντάξει.", "Χαίρετε.") are the weakest case: about 1 in 5 comes out unclear. Prefer "Εντάξει, το κανονίζω." to a bare "Εντάξει.".
• Questions rise at the end more weakly than in natural speech.
• Rare words outside everyday conversation ("πλησιέστερο") are sometimes misread.
• Cypriot could not be scored automatically, because Whisper misreads real Cypriot speech about 46 % of the time. It has been judged by ear only.
• Voice cloning: only clone voices you have permission to use.

License

Weights: CC BY 4.0, the same as the base model kyutai/pocket-tts. Commercial use is allowed with attribution. The bundled voices are cut from Mozilla Common Voice (CC0) recordings; please do not attempt to identify the contributors. As upstream asks: do not use this model to impersonate people or to present generated audio as genuine recordings.

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