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| Property | Value |
|---|---|
| Base checkpoint | en_US-lessac-medium (epoch 2164) |
| Fine-tuned on | 1,500 utterances (~1 hour) of ElevenLabs Sarah audio |
| TTS model used | eleven_turbo_v2_5, stability=0.6, similarity_boost=0.8 |
| Architecture | VITS (Piper medium) |
| Output sample rate | 22,050 Hz |
| ONNX size | ~20MB |
| Training GPU | A100 40GB (GCP spot, ~$12 total) |
| Training epochs | 1,500 |
1echo "Hi, this is Atlas. How can I help you today?" | \
2 piper -m en_US-sarah-atlas.onnx --output_file output.wav1echo "You have 3 urgent emails." | \
2 piper -m en_US-sarah-atlas.onnx --output_raw | aplay -r 22050 -f S16_LE -c 11import subprocess
2result = subprocess.run(
3 ["piper", "-m", "en_US-sarah-atlas.onnx", "--output_raw"],
4 input=b"Let me check your calendar.",
5 capture_output=True
6)
7pcm_audio = result.stdout # 22050Hz mono 16-bit PCMpattern_mapping.py, acknowledgments.py)eleven_turbo_v2_5)099-piper-sarah-voice
Training framework doc: specs/099-piper-sarah-voice/training-framework.md