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.gitignore

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.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0  # Use the latest tag from the repo
+    hooks:
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+        args: [--line-length=100]
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.0.0
+    hooks:
+      - id: flake8
+        args: [--max-line-length=100]

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README.md

@@ -0,0 +1,194 @@
+# NeuTTS Air
+
+||VIDEO||
+
+*Click the image above to watch NeuTTS Air in action on YouTube!*
+
+*Created by [Neuphonic](http://neuphonic.com/) - building faster, smaller, on-device voice AI*
+
+State-of-the-art Voice AI has been locked behind web APIs for too long. NeuTTS Air is the world’s first super-realistic, on-device, TTS speech language model with instant voice cloning. Built off a 0.5B LLM backbone, NeuTTS Air brings natural-sounding speech, real-time performance, built-in security and speaker cloning to your local device - unlocking a new category of embedded voice agents, assistants, toys, and compliance-safe apps.
+
+## Key Features
+
+- 🗣Best-in-class realism for its size - produces natural, ultra-realistic voices that sound human
+- 📱Optimised for on-device deployment - provided in GGML format, ready to run on phones, laptops, or even Raspberry Pis
+- 👫Instant voice cloning - create your own speaker with as little as 3 seconds of audio
+- 🚄Simple LM + codec architecture built off a 0.5B backbone - the sweet spot between speed, size, and quality for real-world applications
+
+## Model Details
+
+NeuTTS Air is built off Qwen 0.5B - a lightweight yet capable language model optimised for text understanding and generation - as well as a powerful combination of technologies designed for efficiency and quality:
+
+- **Audio Codec**: [NeuCodec](https://huggingface.co/neuphonic/neucodec) - our proprietary neural audio codec that achieves exceptional audio quality at low bitrates using a single codebook
+- **Format**: Available in GGML format for efficient on-device inference
+- **Responsibility**: Watermarked outputs
+- **Inference Speed**: Real-time generation on mid-range devices
+- **Power Consumption**: Optimised for mobile and embedded devices
+
+## Get Started
+
+1. **Clone Git Repo**
+   ```bash
+   git clone https://github.com/neuphonic/neutts-air.git
+   ```
+   ```bash
+   cd neuttsair
+   ```
+
+2. **Install `espeak` (required dependency)**
+
+   Please refer to the following link for instructions on how to install `espeak`:
+
+   https://github.com/espeak-ng/espeak-ng/blob/master/docs/guide.md
+
+   ```bash
+   # Mac OS
+   brew install espeak
+
+   # Ubuntu/Debian
+   sudo apt install espeak
+   ```
+
+   Mac users may need to put the following lines at the top of the neutts.py file.
+   ```python
+   from phonemizer.backend.espeak.wrapper import EspeakWrapper
+   _ESPEAK_LIBRARY = '/opt/homebrew/Cellar/espeak/1.48.04_1/lib/libespeak.1.1.48.dylib'  #use the Path to the library.
+   EspeakWrapper.set_library(_ESPEAK_LIBRARY)
+   ```
+
+3. **Install Python dependencies**
+
+   The requirements file includes the dependencies needed to run the model with PyTorch.
+   When using an ONNX decoder or a GGML model, some dependencies (such as PyTorch) are no longer required.
+
+   The inference is compatible and tested on `python>=3.11`.
+
+    ```
+    pip install -r requirements.txt
+    ```
+
+4. **(Optional) Install Llama-cpp-python to use the `GGUF` models.**
+   ```
+   pip install llama-cpp-python
+   ```
+   To run llama-cpp with GPU suport (CUDA, MPS) support please refer to:
+   https://pypi.org/project/llama-cpp-python/
+
+5. **(Optional) Install onnxruntime to use the `.onnx` decoder.**
+   If you wnat to run the onnxdecoder
+   ```
+   pip install onnxruntime
+   ```
+
+## Basic Example
+
+Run the basic example script to synthesize speech:
+```bash
+python -m examples.basic_example \
+  --input_text "My name is Dave, and um, I'm from London" \
+  --ref_audio samples/dave.wav \
+  --ref_text samples/dave.txt
+```
+
+To specify a particular model repo for the backbone or codec, add the `--backbone` argument. Available backbones are listed in [NeuTTS-Air huggingface collection](https://huggingface.co/collections/neuphonic/neutts-air-68cc14b7033b4c56197ef350).
+
+Several examples are available, including a Jupyter notebook in the `examples` folder.
+
+### Simple One-Code Block Usage
+
+```python
+from neuttsair.neutts import NeuTTSAir
+import soundfile as sf
+
+tts = NeuTTSAir( backbone_repo="neuphonic/neutts-air-q4-gguf", backbone_device="cpu", codec_repo="neuphonic/neucodec", codec_device="cpu")
+input_text = "My name is Dave, and um, I'm from London."
+
+ref_text = "samples/dave.txt"
+ref_audio_path = "samples/dave.wav"
+
+ref_text = open(ref_text, "r").read().strip()
+ref_codes = tts.encode_reference(ref_audio_path)
+
+wav = tts.infer(input_text, ref_codes, ref_text)
+sf.write("test.wav", wav, 24000)
+```
+
+
+## Advanced Examples
+### GGML Backbone Example
+```bash
+python -m examples.basic_example \
+  --input_text "My name is Dave, and um, I'm from London" \
+  --ref_audio ./samples/dave.wav \
+  --ref_text ./samples/dave.txt \
+  --backbone neuphonic/neutts-air-q4-gguf
+```
+
+### Onnx Decoder Example
+
+Make sure you have installed ```onnxruntime```
+
+```bash
+python -m examples.onnx_example \
+  --input_text "My name is Dave, and um, I'm from London" \
+  --ref_codes samples/dave.pt \
+  --ref_text samples/dave.txt
+```
+
+To run the model with the onnx decoder you need to encode the reference sample. Please refer to the encode_reference example.
+
+#### Encode reference
+You only need to provide a reference audio for the reference encoding.
+
+```bash
+python -m examples.encode_reference \
+ --ref_audio  ./samples/dave.wav \
+ --output_path encoded_reference.pt
+ ```
+
+## Prepare References for Cloning
+
+NeuTTS Air requires two inputs:
+
+1. A reference audio sample (`.wav` file)
+2. A text string
+
+The model then synthesises the text as speech in the style of the reference audio. This is what enables NeuTTS Air’s instant voice cloning capability.
+
+### Example Reference Files
+
+You can find some ready-to-use samples in the `examples` folder:
+
+- `samples/dave.wav`
+- `samples/jo.wav`
+
+### Guidelines for Best Results
+
+For optimal performance, reference audio samples should be:
+
+1. **Mono channel**
+2. **16-44 kHz sample rate**
+3. **3–15 seconds in length**
+4. **Saved as a `.wav` file**
+5. **Clean** — minimal to no background noise
+6. **Natural, continuous speech** — like a monologue or conversation, with few pauses, so the model can capture tone effectively
+
+## Responsibility
+
+Every audio file generated by NeuTTS Air includes [Perth (Perceptual Threshold) Watermarker](https://github.com/resemble-ai/perth).
+
+## Disclaimer
+
+Don't use this model to do bad things… please.
+
+## Developer Requirements
+
+To run the pre commit hooks to contribute to this project run:
+
+```bash
+pip install pre-commit
+```
+Then:
+```bash
+pre-commit install
+```

examples/basic_example.py

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+import os
+import soundfile as sf
+from neuttsair.neutts import NeuTTSAir
+
+
+def main(input_text, ref_audio_path, ref_text, backbone, output_path="output.wav"):
+    if not ref_audio_path or not ref_text:
+        print("No reference audio or text provided.")
+        return None
+
+    # Initialize NeuTTSAir with the desired model and codec
+    tts = NeuTTSAir(
+        backbone_repo=backbone,
+        backbone_device="cpu",
+        codec_repo="neuphonic/neucodec",
+        codec_device="cpu"
+    )
+
+    # Check if ref_text is a path if it is read it if not just return string
+    if ref_text and os.path.exists(ref_text):
+        with open(ref_text, "r") as f:
+            ref_text = f.read().strip()
+
+    print("Encoding reference audio")
+    ref_codes = tts.encode_reference(ref_audio_path)
+
+    print(f"Generating audio for input text: {input_text}")
+    wav = tts.infer(input_text, ref_codes, ref_text)
+
+    print(f"Saving output to {output_path}")
+    sf.write(output_path, wav, 24000)
+
+
+if __name__ == "__main__":
+    # get arguments from command line
+    import argparse
+
+    parser = argparse.ArgumentParser(description="NeuTTSAir Example")
+    parser.add_argument(
+        "--input_text", 
+        type=str, 
+        required=True, 
+        help="Input text to be converted to speech"
+    )
+    parser.add_argument(
+        "--ref_audio", 
+        type=str, 
+        default="./samples/dave.wav", 
+        help="Path to reference audio file"
+    )
+    parser.add_argument(
+        "--ref_text",
+        type=str,
+        default="./samples/dave.txt", 
+        help="Reference text corresponding to the reference audio",
+    )
+    parser.add_argument(
+        "--output_path", 
+        type=str, 
+        default="output.wav", 
+        help="Path to save the output audio"
+    )
+    parser.add_argument(
+        "--backbone", 
+        type=str, 
+        default="neuphonic/neutts-air", 
+        help="Huggingface repo containing the backbone checkpoint"
+    )
+    args = parser.parse_args()
+    main(
+        input_text=args.input_text,
+        ref_audio_path=args.ref_audio,
+        ref_text=args.ref_text,
+        backbone=args.backbone,
+        output_path=args.output_path,
+    )

examples/encode_reference.py

@@ -0,0 +1,47 @@
+# This file contains an example of how to use the NeuTTSAir class to generate codes
+
+import torch
+from librosa import load
+from neucodec import NeuCodec
+
+
+def main(ref_audio_path, output_path="output.wav"):
+    print("Encoding reference audio")
+
+    # Make sure output path ends with .pt
+    if not output_path.endswith(".pt"):
+        print("Output path should end with .pt to save the codes.")
+        return
+
+    # Initialize codec
+    codec = NeuCodec.from_pretrained("neuphonic/neucodec")
+    codec.eval().to("cpu")
+
+    # Load and encode reference audio
+    wav, _ = load(ref_audio_path, sr=16000, mono=True)  # load as 16kHz
+    wav_tensor = torch.from_numpy(wav).float().unsqueeze(0).unsqueeze(0)  # [1, 1, T]
+    ref_codes = codec.encode_code(audio_or_path=wav_tensor).squeeze(0).squeeze(0)
+
+    # Save the codes
+    torch.save(ref_codes, output_path)
+
+
+if __name__ == "__main__":
+    # get arguments from command line
+    import argparse
+
+    parser = argparse.ArgumentParser(description="NeuTTSAir Reference Encoding Example")
+    parser.add_argument(
+        "--ref_audio", type=str, default="./samples/dave.wav", help="Path to reference audio"
+    )
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        default="encoded_reference.pt",
+        help="Path to save the output codes",
+    )
+    args = parser.parse_args()
+    main(
+        ref_audio_path=args.ref_audio,
+        output_path=args.output_path,
+    )

examples/interactive_example.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a0fa9718",
+   "metadata": {},
+   "source": [
+    "Import required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "939f4fdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "#Append the parent directory to the sys.path to allow imports from neuttsair package\n",
+    "sys.path.append('..')\n",
+    "from neuttsair.neutts import NeuTTSAir\n",
+    "from IPython.display import Audio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4e61937",
+   "metadata": {},
+   "source": [
+    "Downloads files and loads the model into memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfabf9dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "tts = NeuTTSAir(\n",
+    "    backbone_repo=\"neuphonic/neutts-air-q8-gguf\",\n",
+    "    backbone_device=\"cpu\",\n",
+    "    codec_repo=\"neuphonic/neucodec\",\n",
+    "    codec_device=\"cpu\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75c87818",
+   "metadata": {},
+   "source": [
+    "Pick your speaker and type up your input text - and generate!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "914e5e00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "speaker = \"dave\" # default speakers are 'dave' and 'jo'\n",
+    "input_text = \"Hey there, I'm Andy. I'm 25 and I just moved to London. The underground is pretty confusing, but it gets me around in no time at all.\"\n",
+    "\n",
+    "ref_text = f\"../samples/{speaker}.txt\"\n",
+    "ref_audio_path = f\"../samples/{speaker}.wav\"\n",
+    "\n",
+    "ref_text = open(ref_text, \"r\").read().strip()\n",
+    "ref_codes = tts.encode_reference(ref_audio_path)\n",
+    "wav = tts.infer(input_text, ref_codes, ref_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8da4a9d",
+   "metadata": {},
+   "source": [
+    "Listen to your generation!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21774af1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Audio(wav, rate=24000)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/onnx_example.py

@@ -0,0 +1,77 @@
+import os
+import soundfile as sf
+import torch
+from neuttsair.neutts import NeuTTSAir
+
+
+def main(input_text, ref_codes_path, ref_text, backbone, output_path="output.wav"):
+    if not ref_codes_path or not ref_text:
+        print("No reference audio or text provided.")
+        return None
+
+    # Initialize NeuTTSAir with the desired model and codec
+    tts = NeuTTSAir(
+        backbone_repo=backbone,
+        backbone_device="cpu",
+        codec_repo="neuphonic/neucodec-onnx-decoder",
+        codec_device="cpu"
+    )
+
+    # Check if ref_text is a path if it is read it if not just return string
+    if ref_text and os.path.exists(ref_text):
+        with open(ref_text, "r") as f:
+            ref_text = f.read().strip()
+
+    if ref_codes_path and os.path.exists(ref_codes_path):
+        ref_codes = torch.load(ref_codes_path)
+
+    print(f"Generating audio for input text: {input_text}")
+    wav = tts.infer(input_text, ref_codes, ref_text)
+
+    print(f"Saving output to {output_path}")
+    sf.write(output_path, wav, 24000)
+
+
+if __name__ == "__main__":
+    # get arguments from command line
+    import argparse
+
+    parser = argparse.ArgumentParser(description="NeuTTSAir Example")
+    parser.add_argument(
+        "--input_text", 
+        type=str, 
+        required=True, 
+        help="Input text to be converted to speech"
+    )
+    parser.add_argument(
+        "--ref_codes", 
+        type=str, 
+        default="./samples/dave.pt", 
+        help="Path to pre-encoded reference audio"
+    )
+    parser.add_argument(
+        "--ref_text",
+        type=str,
+        default="./samples/dave.txt", 
+        help="Reference text corresponding to the reference audio",
+    )
+    parser.add_argument(
+        "--output_path", 
+        type=str, 
+        default="output.wav", 
+        help="Path to save the output audio"
+    )
+    parser.add_argument(
+        "--backbone", 
+        type=str, 
+        default="neuphonic/neutts-air", 
+        help="Huggingface repo containing the backbone checkpoint"
+    )
+    args = parser.parse_args()
+    main(
+        input_text=args.input_text,
+        ref_codes_path=args.ref_codes,
+        ref_text=args.ref_text,
+        backbone=args.backbone,
+        output_path=args.output_path,
+    )

neuttsair/neutts.py

@@ -0,0 +1,241 @@
+from pathlib import Path
+import librosa
+import numpy as np
+import torch
+import re
+import perth
+from neucodec import NeuCodec, DistillNeuCodec
+from phonemizer.backend import EspeakBackend
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+class NeuTTSAir:
+
+    def __init__(
+        self,
+        backbone_repo="neuphonic/neutts-air",
+        backbone_device="cpu",
+        codec_repo="neuphonic/neucodec",
+        codec_device="cpu",
+    ):
+
+        # Consts
+        self.sample_rate = 24_000
+        self.max_context = 2048
+
+        # ggml & onnx flags
+        self._grammar = None  # set with a ggml model
+        self._is_quantized_model = False
+        self._is_onnx_codec = False
+
+        # HF tokenizer
+        self.tokenizer = None
+
+        # Load phonemizer + models
+        print("Loading phonemizer...")
+        self.phonemizer = EspeakBackend(
+            language="en-us", preserve_punctuation=True, with_stress=True
+        )
+
+        self._load_backbone(backbone_repo, backbone_device)
+
+        self._load_codec(codec_repo, codec_device)
+
+        # Load watermarker
+        self.watermarker = perth.PerthImplicitWatermarker()
+
+    def _load_backbone(self, backbone_repo, backbone_device):
+        print(f"Loading backbone from: {backbone_repo} on {backbone_device} ...")
+
+        # GGUF loading
+        if backbone_repo.endswith("gguf"):
+
+            try:
+                from llama_cpp import Llama
+            except ImportError as e:
+                raise ImportError(
+                    "Failed to import `llama_cpp`. "
+                    "Please install it with:\n"
+                    "    pip install llama-cpp-python"
+                ) from e
+
+            self.backbone = Llama.from_pretrained(
+                repo_id=backbone_repo,
+                filename="*.gguf",
+                verbose=False,
+                n_gpu_layers=-1 if backbone_device == "gpu" else 0,
+                n_ctx=self.max_context,
+                mlock=True,
+                flash_attn=True if backbone_device == "gpu" else False,
+            )
+            self._is_quantized_model = True
+
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(backbone_repo)
+            self.backbone = AutoModelForCausalLM.from_pretrained(backbone_repo).to(
+                torch.device(backbone_device)
+            )
+
+    def _load_codec(self, codec_repo, codec_device):
+
+        print(f"Loading codec from: {codec_repo} on {codec_device} ...")
+        match codec_repo:
+            case "neuphonic/neucodec":
+                self.codec = NeuCodec.from_pretrained(codec_repo)
+                self.codec.eval().to(codec_device)
+            case "neuphonic/distill-neucodec":
+                self.codec = DistillNeuCodec.from_pretrained(codec_repo)
+                self.codec.eval().to(codec_device)
+            case "neuphonic/neucodec-onnx-decoder":
+
+                if codec_device != "cpu":
+                    raise ValueError("Onnx decoder only currently runs on CPU.")
+
+                try:
+                    from neucodec import NeuCodecOnnxDecoder
+                except ImportError as e:
+                    raise ImportError(
+                        "Failed to import the onnx decoder."
+                        " Ensure you have onnxruntime installed as well as neucodec >= 0.0.4."
+                    ) from e
+
+                self.codec = NeuCodecOnnxDecoder.from_pretrained(codec_repo)
+                self._is_onnx_codec = True
+
+            case _:
+                raise ValueError(
+                    "Invalid codec repo! Must be one of:"
+                    " 'neuphonic/neucodec', 'neuphonic/distill-neucodec',"
+                    " 'neuphonic/neucodec-onnx-decoder'."
+                )
+
+    def infer(self, text: str, ref_codes: np.ndarray | torch.Tensor, ref_text: str) -> np.ndarray:
+        """
+        Perform inference to generate speech from text using the TTS model and reference audio.
+
+        Args:
+            text (str): Input text to be converted to speech.
+            ref_codes (np.ndarray | torch.tensor): Encoded reference.
+            ref_text (str): Reference text for reference audio. Defaults to None.
+        Returns:
+            np.ndarray: Generated speech waveform.
+        """
+
+        # Generate tokens
+        if self._is_quantized_model:
+            output_str = self._infer_ggml(ref_codes, ref_text, text)
+        else:
+            prompt_ids = self._apply_chat_template(ref_codes, ref_text, text)
+            output_str = self._infer_torch(prompt_ids)
+
+        # Decode
+        wav = self._decode(output_str)
+        watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=24_000)
+
+        return watermarked_wav
+
+    def encode_reference(self, ref_audio_path: str | Path):
+        wav, _ = librosa.load(ref_audio_path, sr=16000, mono=True)
+        wav_tensor = torch.from_numpy(wav).float().unsqueeze(0).unsqueeze(0)  # [1, 1, T]
+        ref_codes = self.codec.encode_code(audio_or_path=wav_tensor).squeeze(0).squeeze(0)
+        return ref_codes
+
+    def _decode(self, codes: str):
+
+        # Extract speech token IDs using regex
+        speech_ids = [int(num) for num in re.findall(r"<\|speech_(\d+)\|>", codes)]
+
+        if len(speech_ids) > 0:
+
+            # Onnx decode
+            if self._is_onnx_codec:
+                codes = np.array(speech_ids, dtype=np.int32)[np.newaxis, np.newaxis, :]
+                recon = self.codec.decode_code(codes)
+
+            # Torch decode
+            else:
+                with torch.no_grad():
+                    codes = torch.tensor(speech_ids, dtype=torch.long)[None, None, :].to(
+                        self.codec.device
+                    )
+                    recon = self.codec.decode_code(codes).cpu().numpy()
+
+            return recon[0, 0, :]
+        else:
+            raise ValueError("No valid speech tokens found in the output.")
+
+    def _to_phones(self, text: str) -> str:
+        phones = self.phonemizer.phonemize([text])
+        phones = phones[0].split()
+        phones = " ".join(phones)
+        return phones
+
+    def _apply_chat_template(
+        self, ref_codes: list[int], ref_text: str, input_text: str
+    ) -> list[int]:
+
+        input_text = self._to_phones(ref_text) + " " + self._to_phones(input_text)
+        speech_replace = self.tokenizer.convert_tokens_to_ids("<|SPEECH_REPLACE|>")
+        speech_gen_start = self.tokenizer.convert_tokens_to_ids("<|SPEECH_GENERATION_START|>")
+        text_replace = self.tokenizer.convert_tokens_to_ids("<|TEXT_REPLACE|>")
+        text_prompt_start = self.tokenizer.convert_tokens_to_ids("<|TEXT_PROMPT_START|>")
+        text_prompt_end = self.tokenizer.convert_tokens_to_ids("<|TEXT_PROMPT_END|>")
+
+        input_ids = self.tokenizer.encode(input_text, add_special_tokens=False)
+        chat = """user: Convert the text to speech:<|TEXT_REPLACE|>\nassistant:<|SPEECH_REPLACE|>"""
+        ids = self.tokenizer.encode(chat)
+
+        text_replace_idx = ids.index(text_replace)
+        ids = (
+            ids[:text_replace_idx]
+            + [text_prompt_start]
+            + input_ids
+            + [text_prompt_end]
+            + ids[text_replace_idx + 1 :]  # noqa
+        )
+
+        speech_replace_idx = ids.index(speech_replace)
+        codes_str = "".join([f"<|speech_{i}|>" for i in ref_codes])
+        codes = self.tokenizer.encode(codes_str, add_special_tokens=False)
+        ids = ids[:speech_replace_idx] + [speech_gen_start] + list(codes)
+
+        return ids
+
+    def _infer_torch(self, prompt_ids: list[int]) -> str:
+        prompt_tensor = torch.tensor(prompt_ids).unsqueeze(0).to(self.backbone.device)
+        speech_end_id = self.tokenizer.convert_tokens_to_ids("<|SPEECH_GENERATION_END|>")
+        with torch.no_grad():
+            output_tokens = self.backbone.generate(
+                prompt_tensor,
+                max_length=self.max_context,
+                eos_token_id=speech_end_id,
+                do_sample=True,
+                temperature=1.0,
+                top_k=50,
+                use_cache=True,
+                min_new_tokens=50,
+            )
+        input_length = prompt_tensor.shape[-1]
+        output_str = self.tokenizer.decode(
+            output_tokens[0, input_length:].cpu().numpy().tolist(), add_special_tokens=False
+        )
+        return output_str
+
+    def _infer_ggml(self, ref_codes: list[int], ref_text: str, input_text: str) -> str:
+        ref_text = self._to_phones(ref_text)
+        input_text = self._to_phones(input_text)
+
+        codes_str = "".join([f"<|speech_{idx}|>" for idx in ref_codes])
+        prompt = (
+            f"user: Convert the text to speech:<|TEXT_PROMPT_START|>{ref_text} {input_text}"
+            f"<|TEXT_PROMPT_END|>\nassistant:<|SPEECH_GENERATION_START|>{codes_str}"
+        )
+        output = self.backbone(
+            prompt,
+            max_tokens=self.max_context,
+            temperature=1.0,
+            top_k=50,
+            stop=["<|SPEECH_GENERATION_END|>"],
+        )
+        output_str = output["choices"][0]["text"]
+        return output_str

requirements.txt

@@ -0,0 +1,18 @@
+datasets==4.0.0
+librosa==0.11.0
+neucodec>=0.0.3
+numpy==2.2.6
+pandas==2.3.2
+phonemizer==3.3.0
+requests==2.32.5
+scipy>=1.15
+soundfile==0.13.1
+torch==2.8.0
+torchao==0.13.0
+torchaudio==2.8.0
+torchtune==0.6.1
+tqdm==4.67.1
+transformers==4.56.1
+vector-quantize-pytorch==1.17.8
+resemble-perth==1.0.1
+accelerate==1.10.1

samples/dave.txt

@@ -0,0 +1 @@
+So I'm live on radio. And I say, well, my dear friend James here clearly, and the whole room just froze. Turns out I'd completely misspoken and mentioned our other friend.

samples/jo.txt

@@ -0,0 +1 @@
+So I just tried Neuphonic and I’m genuinely impressed. It's super responsive, it sounds clean, supports voice cloning, and the agent feature is fun to play with too. Highly recommend it for podcasts, conversations, or even just messing around with voiceovers.