Add model card for Sol

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-to-speech
+base_model: microsoft/speecht5_tts
+datasets:
+  - openslr/librispeech_asr
+tags:
+  - text-to-speech
+  - tts
+  - speecht5
+  - sol
+  - speech-synthesis
+---
+
+# Sol
+
+**Sol** is a text-to-speech model that turns written text into natural sounding
+speech. It is a `SpeechT5` acoustic model that was **fine-tuned on public voice
+data** (a single-speaker subset of [LibriSpeech](https://www.openslr.org/12),
+speaker `2277`) and paired with the open `speecht5_hifigan` neural vocoder.
+
+Sol was trained end-to-end by this project: data collection, preprocessing,
+fine-tuning, evaluation and packaging. Training ran on CPU — no GPU required to
+reproduce.
+
+## Model details
+
+| | |
+|---|---|
+| Architecture | SpeechT5 (Transformer encoder–decoder TTS + HiFi-GAN vocoder) |
+| Base checkpoint | [`microsoft/speecht5_tts`](https://huggingface.co/microsoft/speecht5_tts) |
+| Vocoder | [`microsoft/speecht5_hifigan`](https://huggingface.co/microsoft/speecht5_hifigan) |
+| Training data | LibriSpeech `clean` / `validation`, speaker `2277` (public) |
+| Utterances | 87 |
+| Optimizer steps | ~250 (CPU, AdamW, lr 1e-5 → 5e-6) |
+| Sampling rate | 16 kHz |
+| Language | English |
+| Voice | single speaker via a fixed x-vector (`speaker_embedding.npy`) |
+
+## Example audio
+
+These clips were generated by Sol (`<audio>` elements play on the Hub):
+
+<audio controls src="examples/hello.mp3"></audio>
+
+> "hello, i am sol. i can turn any text you give me into natural sounding speech."
+
+<audio controls src="examples/about.mp3"></audio>
+
+> "sol is a small, open text to speech model, fine tuned on public domain voice recordings."
+
+<audio controls src="examples/ai.mp3"></audio>
+
+> "artificial intelligence is changing how we create and interact with sound."
+
+More clips are in the [`examples/`](./examples) folder.
+
+## Usage
+
+```python
+import numpy as np
+import soundfile as sf
+import torch
+from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor
+
+repo = "itriedcoding/Sol"
+
+processor = SpeechT5Processor.from_pretrained(repo)
+model = SpeechT5ForTextToSpeech.from_pretrained(repo)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+speaker = torch.tensor(np.load("speaker_embedding.npy")).unsqueeze(0)  # shipped with the model
+
+text = "hello, i am sol."
+inputs = processor(text=text, return_tensors="pt")
+
+with torch.no_grad():
+    speech = model.generate_speech(inputs["input_ids"], speaker, vocoder=vocoder)
+
+sf.write("sol.wav", speech.numpy(), samplerate=16000)
+```
+
+The `speaker_embedding.npy` file is included in this repository so you get Sol's
+voice without needing a separate speaker-encoding model.
+
+## Intended use & limitations
+
+- **Intended use:** research, prototyping and demos of English speech synthesis.
+- **Limitations:** single English speaker; no multi-speaker or multilingual
+  support; may mispronounce rare names, numbers and unusual punctuation; can
+  occasionally cut a sentence short. Output is not a human voice and should not
+  be presented as one.
+- Do not use for impersonation or deceptive audio.
+
+## Training data & credits
+
+- Voice data: [LibriSpeech](https://www.openslr.org/12) (`openslr/librispeech_asr`),
+  released under CC BY 4.0.
+- Base model & vocoder: Microsoft Research Asia, via 🤗 Transformers.
+- Fine-tuned and packaged by the **Sol** project.
+
+## License
+
+Apache-2.0, following the base model. Please respect the original dataset and
+base-model licenses.