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---
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license: mit
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---
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# Spanish-F5 TTS Inference API (Hugging Face)
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This project exposes a Hugging Face Inference Endpoint for Spanish-F5, a Spanish-adapted version of the F5-TTS model. It takes reference audio and a target sentence, and synthesizes speech in the same voice.
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> ✨ Live inference is powered by Hugging Face Inference Endpoints.
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---
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## 🔗 Credit
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This project is based on [jpgallegoar/Spanish-F5](https://github.com/jpgallegoar/Spanish-F5).
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- Spanish-F5 by [@jpgallegoar](https://github.com/jpgallegoar)
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- [Model weights on Hugging Face](https://huggingface.co/jpgallegoar/F5-Spanish/)
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Addapted by [@eloicito333](https://github.com/eloicito333).
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Licensed under the MIT License.
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---
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## ⚙️ How It Works
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### 🔽 Request Parameters
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Send a POST request with a JSON body to the Hugging Face Inference Endpoint:
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```json
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{
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"ref_audio": "<base64-encoded WAV>", // string, required
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"ref_text": "Hola, ¿cómo estás?", // string, optional (transcript of ref_audio)
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"gen_text": "Estoy muy bien, gracias.", // string, required (text to synthesize)
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"remove_silence": true, // boolean, optional (default: true)
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"speed": 1.0, // number, optional (default: 1.0)
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"cross_fade_duration": 0.15 // number, optional (default: 0.15)
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}
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```
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### 🔼 Response Object
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The response will be a JSON object:
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```json
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{
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"success": true, // boolean: true if synthesis succeeded
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"audio_base64": "<base64-encoded WAV output>" // string: base64 WAV audio (if success)
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}
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```
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If an error occurs:
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```json
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{
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"success": false,
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"error": "TypeError: some descriptive message" // string: error description
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}
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```
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Use the `audio_base64` field to decode and save the resulting audio.
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---
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## 🤖 Node.js Client Example (Using Fetch)
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```js
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import "fs"
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async function sendAudio() {
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const audioBuffer = fs.readFileSync("./example.wav");
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const audioBase64 = audioBuffer.toString("base64");
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const response = await fetch("https://your-hf-endpoint-url", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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ref_audio: audioBase64,
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ref_text: "Hola, ¿cómo estás?",
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gen_text: "Estoy muy bien, gracias.",
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remove_silence: true,
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speed: 1.0,
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cross_fade_duration: 0.15,
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})
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});
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const result = await response.json();
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if (result.audio_base64) {
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fs.writeFileSync("output.wav", Buffer.from(result.audio_base64, "base64"));
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console.log("Audio saved to output.wav");
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} else {
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console.error("Error:", result);
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}
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}
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sendAudio();
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```
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---
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## 🔬 Python Client Example (Optional)
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```python
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import requests
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import base64
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with open("ref.wav", "rb") as f:
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audio_base64 = base64.b64encode(f.read()).decode("utf-8")
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response = requests.post("https://your-hf-endpoint-url", json={
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"ref_audio": audio_base64,
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"ref_text": "Hola, ¿cómo estás?",
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"gen_text": "Estoy muy bien, gracias.",
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"remove_silence": True,
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"speed": 1.0,
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"cross_fade_duration": 0.15
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})
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if response.ok and response.json().get("audio_base64"):
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with open("output.wav", "wb") as out:
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out.write(base64.b64decode(response.json()["audio_base64"]))
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print("Audio saved to output.wav")
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else:
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print("Error:", response.json())
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```
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---
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## 🎓 License
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MIT License. See [LICENSE](./LICENSE) for more information.
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---
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## ✏️ Author
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Addapted by [@eloicito333](https://github.com/eloicito333). |
