---
language:
  - en
license: apache-2.0
base_model: SparkAudio/Spark-TTS-0.5B
datasets:
  - MrDragonFox/Elise
tags:
  - tts
  - text-to-speech
  - spark-tts
  - voice-cloning
  - emotion-tags
  - unsloth
  - trl
  - sft
  - featherlabs
  - audio
  - amd-mi300x
library_name: transformers
pipeline_tag: text-to-speech
---

<div align="center">

# 🔊 Finatts Enhanced

### *High-fidelity voice cloning — fine-tuned Spark-TTS v2*

**Text-to-Speech · Voice Cloning · Emotion Tags · Portable Voice Profile**

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Base Model](https://img.shields.io/badge/Base-Spark--TTS--0.5B-purple)](https://huggingface.co/SparkAudio/Spark-TTS-0.5B)
[![Dataset](https://img.shields.io/badge/Dataset-MrDragonFox%2FElise-green)](https://huggingface.co/datasets/MrDragonFox/Elise)
[![Parameters](https://img.shields.io/badge/Params-507M-orange)](https://huggingface.co/Featherlabs/Finatts-enhanced)

*Built by [Featherlabs](https://huggingface.co/Featherlabs) · Operated by Owlkun*

</div>

---

## ✨ What is Finatts Enhanced?

Finatts Enhanced is an improved **507M-parameter text-to-speech model** built on [Spark-TTS-0.5B](https://huggingface.co/SparkAudio/Spark-TTS-0.5B), fine-tuned for **high-fidelity single-speaker voice cloning** with emotion tag support.

Compared to the original [Finatts](https://huggingface.co/Featherlabs/Finatts), this version features **3× the training**, a more stable learning rate, and a portable voice profile (`elise_voice.safetensors`) — no reference audio needed at inference time.

### Improvements over v1

| Setting | Finatts v1 | Finatts Enhanced |
|:---|:---:|:---:|
| Epochs | 2 | **6** |
| Learning rate | 1e-4 | **5e-5** |
| Warmup steps | 20 | **50** |
| Weight decay | 0.001 | **0.01** |
| Emotion tags | ❌ | **✅** |
| Voice profile | ❌ | **✅ `elise_voice.safetensors`** |
| Final loss | 5.827 | **5.806** |

### 🎯 Built For

| Capability | Description |
|:---:|---|
| 🎙️ **Voice Cloning** | Clone Elise's voice — no reference audio required |
| 🎭 **Emotion Tags** | `<laughs>` `<giggles>` `<whispers>` `<sighs>` `<chuckles>` `<long pause>` |
| 📝 **Text-to-Speech** | Convert text to natural, expressive speech |
| 📦 **Portable Profile** | Load `elise_voice.safetensors` — deploy anywhere |

---

## 🏋️ Training Details

<table>
<tr><td><b>Property</b></td><td><b>Value</b></td></tr>
<tr><td>Base model</td><td><a href="https://huggingface.co/SparkAudio/Spark-TTS-0.5B">SparkAudio/Spark-TTS-0.5B</a></td></tr>
<tr><td>LLM backbone</td><td>Qwen2-0.5B (507M params)</td></tr>
<tr><td>Dataset</td><td><a href="https://huggingface.co/datasets/MrDragonFox/Elise">MrDragonFox/Elise</a> (1,195 samples, ~3h)</td></tr>
<tr><td>Training type</td><td>Full Supervised Fine-Tuning (SFT)</td></tr>
<tr><td>Epochs</td><td>6</td></tr>
<tr><td>Batch size</td><td>8 (effective 16 with grad accum)</td></tr>
<tr><td>Learning rate</td><td>5e-5</td></tr>
<tr><td>Warmup steps</td><td>50</td></tr>
<tr><td>Weight decay</td><td>0.01</td></tr>
<tr><td>Context length</td><td>4,096 tokens</td></tr>
<tr><td>Precision</td><td>BF16</td></tr>
<tr><td>Optimizer</td><td>AdamW (torch fused)</td></tr>
<tr><td>LR scheduler</td><td>Cosine</td></tr>
<tr><td>Framework</td><td>Unsloth + TRL (SFTTrainer)</td></tr>
<tr><td>Hardware</td><td>AMD MI300X (192GB HBM3)</td></tr>
</table>

### 📊 Training Metrics

| Metric | Value |
|:---|:---:|
| **Final loss** | 5.806 |
| **Training time** | 144s (2.4 min) |
| **Peak VRAM** | 22.5 GB (11.7% of 192GB) |
| **Trainable params** | 506,634,112 (100%) |
| **Total steps** | 450 |

### Training Loss Curve

Model converges from **~6.9 → ~5.8** over 450 steps — 3× more convergence than v1:

| Step | Loss | Step | Loss | Step | Loss |
|:---:|:---:|:---:|:---:|:---:|:---:|
| 1 | 6.90 | 150 | 5.79 | 300 | 5.74 |
| 50 | 5.82 | 200 | 5.76 | 400 | 5.77 |
| 100 | 5.77 | 250 | 5.73 | 450 | 5.81 |

---

## 🚀 Quick Start

### Prerequisites

```bash
pip install "unsloth[amd] @ git+https://github.com/unslothai/unsloth"
pip install "transformers<=5.2.0,>=4.51.3" "trl<=0.24.0,>=0.18.2"
pip install omegaconf einx "datasets>=3.4.1,<4.4.0" soundfile safetensors

# Clone Spark-TTS for BiCodec tokenizer
git clone https://github.com/SparkAudio/Spark-TTS
```

### Inference with Elise Voice Profile

```python
import torch, re, sys
import soundfile as sf
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import snapshot_download, hf_hub_download
from safetensors.torch import load_file
import json

sys.path.append("Spark-TTS")
from sparktts.models.audio_tokenizer import BiCodecTokenizer

MODEL_ID = "Featherlabs/Finatts-enhanced"

# Load LLM
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto"
)
model.eval()

# Load BiCodec
snapshot_download("unsloth/Spark-TTS-0.5B", local_dir="Spark-TTS-0.5B")
audio_tokenizer = BiCodecTokenizer("Spark-TTS-0.5B", "cuda")

# Load Elise voice profile (global token IDs — no reference audio needed)
profile_path = hf_hub_download(MODEL_ID, "elise_voice_profile.json")
with open(profile_path) as f:
    profile = json.load(f)
elise_global_ids       = profile["global_token_ids"]
elise_global_token_str = profile["global_token_str"]


@torch.inference_mode()
def generate_speech(text, temperature=0.8, top_k=40, top_p=0.92):
    prompt = "".join([
        "<|task_tts|>",
        "<|start_content|>", text, "<|end_content|>",
        "<|start_global_token|>",
        elise_global_token_str,       # Elise's voice injected here
        "<|end_global_token|>",
        "<|start_semantic_token|>",
    ])
    inputs    = tokenizer([prompt], return_tensors="pt").to("cuda")
    generated = model.generate(
        **inputs, max_new_tokens=2048,
        do_sample=True, temperature=temperature,
        top_k=top_k, top_p=top_p,
        eos_token_id=tokenizer.eos_token_id,
    )
    out = tokenizer.batch_decode(
        generated[:, inputs.input_ids.shape[1]:], skip_special_tokens=False
    )[0]
    sem = [int(t) for t in re.findall(r"bicodec_semantic_(\d+)", out)]
    if not sem:
        return None
    pred_sem    = torch.tensor(sem, dtype=torch.long).unsqueeze(0).to("cuda")
    pred_global = torch.tensor(elise_global_ids, dtype=torch.long).unsqueeze(0).to("cuda")
    audio_tokenizer.model.to("cuda")
    return audio_tokenizer.detokenize(pred_global, pred_sem).squeeze().cpu().numpy()


# Try emotion tags!
texts = [
    "Hey there! My name is Elise, nice to meet you.",
    "<laughs> Oh my gosh, I can't believe that actually worked!",
    "<whispers> Come closer... I have a secret to tell you.",
    "<sighs> Some days just feel heavier than others.",
]
for i, text in enumerate(texts):
    wav = generate_speech(text)
    if wav is not None:
        sf.write(f"output_{i+1}.wav", wav, 16000)
        print(f"✅ output_{i+1}.wav")
```

---

## 🎭 Emotion Tags

The Elise dataset includes inline emotion tags captured from real speech. Place them anywhere in your text:

| Tag | Effect |
|:---|:---|
| `<laughs>` | Lighter, brighter intonation |
| `<giggles>` | Playful, uptick in pitch |
| `<whispers>` | Softer, breathier delivery |
| `<sighs>` | Drawn-out, melancholic tone |
| `<chuckles>` | Gentle amusement |
| `<long pause>` | Extended pause in speech |

**Note:** Tags produce **intonation variation** rather than literal acoustic sounds (e.g., actual giggling audio). For acoustic emotion effects, see [Orpheus-TTS](https://huggingface.co/canopylabs/orpheus-3b-0.1-ft).

---

## 🏗️ Architecture

```
Text + Emotion Tags
        ↓
  [LLM: Qwen2-0.5B]
  ┌─────┴──────┐
  Global tokens   Semantic tokens
  (speaker ID)    (content + prosody)
       └────────┬────────┘
         [BiCodec Decoder]
                ↓
           Waveform 16kHz
```

| Component | Details |
|:---|:---|
| **LLM** | Qwen2-0.5B (507M params) |
| **BiCodec** | Neural audio codec — global + semantic tokenization |
| **Wav2Vec2** | `wav2vec2-large-xlsr-53` — feature extraction |
| **Sample rate** | 16kHz |
| **Voice profile** | `elise_voice.safetensors` — 1024-dim d-vector |

---

## 📦 Repository Files

| File | Description |
|:---|:---|
| `model.safetensors` | Fine-tuned LLM weights (966MB, 16-bit merged) |
| `elise_voice.safetensors` | Elise speaker d-vector (1024-dim, avg of 10 clips) |
| `tokenizer.json` | Tokenizer including BiCodec special tokens |
| `config.json` | Model configuration |

For inference you also need:

| File | Source |
|:---|:---|
| BiCodec model | [`unsloth/Spark-TTS-0.5B`](https://huggingface.co/unsloth/Spark-TTS-0.5B) |
| Spark-TTS code | [SparkAudio/Spark-TTS](https://github.com/SparkAudio/Spark-TTS) |

---

## ⚠️ Limitations

- **English only** — only tested with English text inputs
- **Single speaker** — optimized for Elise; base model multi-speaker may be degraded
- **16kHz output** — use [audiosr](https://github.com/haoheliu/versatile_audio_super_resolution) for upsampling to 44.1kHz
- **Emotion intensity** — tags produce subtle intonation changes, not acoustic emotion sounds
- **ROCm-trained** — tested on AMD MI300X; CUDA users may need minor env adjustments

---

## 🔮 What's Next

- 🔊 **Super-resolution** — integrate audiosr for 44.1kHz HD output
- 🗣️ **Multi-speaker** — train on multiple voices
- 📈 **Larger dataset** — more hours of Elise audio for stronger emotion control
- 🎭 **Acoustic emotions** — explore Orpheus-style explicit emotion tokens

---

## 📜 License

Apache 2.0 — consistent with [Spark-TTS-0.5B](https://huggingface.co/SparkAudio/Spark-TTS-0.5B).

---

<div align="center">

**Built with ❤️ by [Featherlabs](https://huggingface.co/Featherlabs)**

*Operated by Owlkun*

</div>