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README.md

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+# SimpleTTS
+
+Decoder-only transformer TTS, using [WavTokenizer](https://github.com/jishengpeng/WavTokenizer)
+as the audio codec.
+
+## Samples
+
+| Text     | Audio                                                                                                                        |
+| -------- | ---------------------------------------------------------------------------------------------------------------------------- |
+| Sample 1 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/English-TTS/resolve/main/sample_audios/sample_1.wav"></audio> |
+| Sample 2 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/English-TTS/resolve/main/sample_audios/sample_2.wav"></audio> |
+| Sample 3 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/English-TTS/resolve/main/sample_audios/sample_3.wav"></audio> |
+| Sample 4 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/English-TTS/resolve/main/sample_audios/sample_4.wav"></audio> |
+| Sample 5 | <audio controls src="https://huggingface.co/AnotherPotatoCoder/English-TTS/resolve/main/sample_audios/sample_5.wav"></audio> |
+
+## Setup
+
+```bash
+git clone https://github.com/FirstPotatoCoder/SimpleTTS.git
+cd SimpleTTS
+bash setup.sh
+python scripts/download_weights.py
+```
+
+## Run
+
+```bash
+python examples/run_inference.py "Hello, this is a quick test-run. I'm checking whether the model can handle longer input text without any issues. This sentence is roughly five times the length of the original short test phrase."
+```
+
+Or from Python:
+
+```python
+from tts.inference import TTSPipeline
+
+pipe = TTSPipeline(
+tts_weights="weights/tts.pt",
+wavtokenizer_weights="weights/wavtokenizer.ckpt",
+wavtokenizer_config="configs/wavtokenizer_config.yaml",
+)
+pipe.generate("Some text to speak.", out_path="out.wav")
+```
+
+## Limitations
+
+- The model sometimes hallucinates, generating babbling or garbled output on rare or unseen words — likely due to the limited amount of training data.
+- Works best when generating ~3s to 15s of audio, matching the length distribution of its training data.
+- No chunking support yet — the current repo only supports clip-by-clip generation, one sample at a time.
+
+## Credits
+
+Audio tokenizer/codec: [WavTokenizer](https://github.com/jishengpeng/WavTokenizer)
+(vendored under `wavtokenizer/`, trimmed to inference-only code).
+
+Data synthesis: [Kokoro](https://github.com/hexgrad/kokoro), used to generate ~100 hours
+of synthetic audio for training this TTS model.
+
+Text phonemization: [espeak-ng](https://github.com/espeak-ng/espeak-ng), used to
+convert input text into phonemes.