XTTS (Coqui)

Generate natural speech with voice cloning using Coqui XTTS.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is XTTS?

XTTS (by Coqui) offers:

• High-quality text-to-speech

• Voice cloning from 6 seconds of audio

• 17 languages supported

• Emotional control

• Streaming support

Requirements

Mode	VRAM	Recommended
Inference	4GB	RTX 3060
Fast Inference	6GB	RTX 3080
Streaming	4GB	RTX 3060

Quick Deploy

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime

Ports:

22/tcp
8000/http

Command:

pip install TTS && \
tts-server --model_name tts_models/multilingual/multi-dataset/xtts_v2

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

pip install TTS

Basic Usage

Simple TTS

from TTS.api import TTS

# Load XTTS v2
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")

# Generate speech
tts.tts_to_file(
    text="Hello, this is a test of the XTTS text to speech system.",
    file_path="output.wav",
    language="en"
)

Voice Cloning

from TTS.api import TTS

tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")

# Clone voice from reference audio (6+ seconds)
tts.tts_to_file(
    text="This is my cloned voice speaking new text.",
    file_path="cloned_output.wav",
    speaker_wav="reference_voice.wav",
    language="en"
)

Multiple Languages

from TTS.api import TTS

tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")

# English
tts.tts_to_file(
    text="Hello, how are you today?",
    file_path="english.wav",
    speaker_wav="voice.wav",
    language="en"
)

# Spanish
tts.tts_to_file(
    text="Hola, ¿cómo estás hoy?",
    file_path="spanish.wav",
    speaker_wav="voice.wav",
    language="es"
)

# German
tts.tts_to_file(
    text="Hallo, wie geht es dir heute?",
    file_path="german.wav",
    speaker_wav="voice.wav",
    language="de"
)

# Russian
tts.tts_to_file(
    text="Привет, как дела?",
    file_path="russian.wav",
    speaker_wav="voice.wav",
    language="ru"
)

Supported Languages

Code	Language
en	English
es	Spanish
fr	French
de	German
it	Italian
pt	Portuguese
pl	Polish
tr	Turkish
ru	Russian
nl	Dutch
cs	Czech
ar	Arabic
zh-cn	Chinese
ja	Japanese
hu	Hungarian
ko	Korean
hi	Hindi

Streaming TTS

from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
import torch
import sounddevice as sd

# Load model
config = XttsConfig()
config.load_json("path/to/config.json")
model = Xtts.init_from_config(config)
model.load_checkpoint(config, checkpoint_dir="path/to/model")
model.cuda()

# Get speaker embedding
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
    audio_path="reference.wav"
)

# Stream generation
chunks = model.inference_stream(
    text="This is a streaming test of the XTTS system.",
    language="en",
    gpt_cond_latent=gpt_cond_latent,
    speaker_embedding=speaker_embedding,
    stream_chunk_size=20
)

# Play in real-time
for chunk in chunks:
    audio = chunk.cpu().numpy()
    sd.play(audio, samplerate=24000)
    sd.wait()

Gradio Interface

import gradio as gr
from TTS.api import TTS
import tempfile

tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")

def generate_speech(text, reference_audio, language):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        if reference_audio:
            tts.tts_to_file(
                text=text,
                file_path=f.name,
                speaker_wav=reference_audio,
                language=language
            )
        else:
            tts.tts_to_file(
                text=text,
                file_path=f.name,
                language=language
            )
        return f.name

demo = gr.Interface(
    fn=generate_speech,
    inputs=[
        gr.Textbox(label="Text to speak", lines=5),
        gr.Audio(type="filepath", label="Reference Voice (optional)"),
        gr.Dropdown(
            ["en", "es", "fr", "de", "it", "pt", "ru", "zh-cn", "ja"],
            value="en",
            label="Language"
        )
    ],
    outputs=gr.Audio(label="Generated Speech"),
    title="XTTS Voice Cloning"
)

demo.launch(server_name="0.0.0.0", server_port=7860)

API Server

from fastapi import FastAPI, UploadFile, File, Form
from fastapi.responses import FileResponse
from TTS.api import TTS
import tempfile
import os

app = FastAPI()
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")

@app.post("/synthesize")
async def synthesize(
    text: str = Form(...),
    language: str = Form(default="en"),
    speaker: UploadFile = File(default=None)
):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as out_file:
        if speaker:
            # Save uploaded reference
            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as ref_file:
                ref_file.write(await speaker.read())
                ref_path = ref_file.name

            tts.tts_to_file(
                text=text,
                file_path=out_file.name,
                speaker_wav=ref_path,
                language=language
            )
            os.unlink(ref_path)
        else:
            tts.tts_to_file(
                text=text,
                file_path=out_file.name,
                language=language
            )

        return FileResponse(out_file.name, media_type="audio/wav")

# Run: uvicorn server:app --host 0.0.0.0 --port 8000

Batch Processing

from TTS.api import TTS
import os

tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to("cuda")

texts = [
    "Welcome to our platform.",
    "Please review the following information.",
    "Thank you for your attention.",
    "Have a great day!"
]

reference_voice = "speaker.wav"
output_dir = "./audio_files"
os.makedirs(output_dir, exist_ok=True)

for i, text in enumerate(texts):
    output_path = f"{output_dir}/audio_{i:03d}.wav"

    tts.tts_to_file(
        text=text,
        file_path=output_path,
        speaker_wav=reference_voice,
        language="en"
    )

    print(f"Generated: {output_path}")

Fine-tuning Voice

For better voice cloning:

from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts

config = XttsConfig()
model = Xtts.init_from_config(config)

# Use multiple reference samples for better quality
reference_files = [
    "sample1.wav",
    "sample2.wav",
    "sample3.wav"
]

# Extract speaker embedding from multiple samples
gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
    audio_path=reference_files
)

# Generate with averaged embedding
output = model.inference(
    text="High quality cloned speech.",
    language="en",
    gpt_cond_latent=gpt_cond_latent,
    speaker_embedding=speaker_embedding
)

Audio Preprocessing

import librosa
import soundfile as sf

def prepare_reference(input_path, output_path, target_sr=22050):
    # Load and resample
    audio, sr = librosa.load(input_path, sr=target_sr)

    # Trim silence
    audio, _ = librosa.effects.trim(audio, top_db=20)

    # Normalize
    audio = librosa.util.normalize(audio)

    # Save
    sf.write(output_path, audio, target_sr)

# Prepare reference audio
prepare_reference("raw_voice.wav", "clean_voice.wav")

Performance

Mode	GPU	Speed
Standard	RTX 3060	~0.5x realtime
Standard	RTX 4090	~2x realtime
Streaming	RTX 3060	~1x realtime
Streaming	RTX 4090	~3x realtime

Quality Tips

• Use 6-15 seconds of clean reference audio

• Avoid background noise in reference

• Match language of text and reference

• Use multiple reference samples for better results

Troubleshooting

Poor Voice Quality

• Clean reference audio

• Longer reference (10+ seconds)

• Match speaking style

Wrong Language Pronunciation

• Ensure correct language code

• Use native speaker reference

Slow Generation

• Enable GPU inference

• Use streaming mode

• Reduce text length per call

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• Bark TTS - Expressive TTS

• SadTalker - Talking heads

• RVC Voice Clone - Voice conversion