TTS Engine Comparison

Compare the leading open-source text-to-speech engines for deployment on Clore.ai GPU servers.

Text-to-Speech (TTS) converts written text into natural-sounding audio. This guide compares five leading open-source TTS engines: XTTS v2, Bark, Kokoro, Fish Speech, and MeloTTS — covering quality, speed, language support, and voice cloning capabilities.

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Quick Decision Matrix

	XTTS v2	Bark	Kokoro	Fish Speech	MeloTTS
Developer	Coqui AI	Suno AI	Hexgrad	Fish Audio	MyShell AI
Quality	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐
Speed	Medium	Slow	Fast	Fast	Fastest
Voice cloning	✅ (3s clip)	✅ (voice presets)	✅ (limited)	✅ (10s clip)	❌
Languages	17	10+	English	8+	8
Min VRAM	4GB	8GB	CPU ok	4GB	CPU ok
License	CPML (non-commercial)	MIT	Apache 2.0	CC BY-NC-SA	MIT
GitHub stars	35K+ (Coqui TTS)	38K+	12K+	14K+	15K+

---

Overview

XTTS v2

Coqui's XTTS v2 is the gold standard for open-source voice cloning TTS. It can clone any voice from a 3-second audio clip with exceptional fidelity.

Philosophy: Maximum expressiveness and voice cloning quality.

from TTS.api import TTS

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

# Zero-shot voice cloning from 3s reference
tts.tts_to_file(
    text="Hello, this is a cloned voice speaking naturally.",
    speaker_wav="reference_voice.wav",
    language="en",
    file_path="output.wav"
)

Bark

Suno's Bark is a transformer-based TTS model that generates highly expressive speech, including non-speech sounds: laughter, sighs, music, and sound effects.

Philosophy: Not just speech — full audio generation.

from bark import SAMPLE_RATE, generate_audio, preload_models
from scipy.io.wavfile import write as write_wav

preload_models()

audio_array = generate_audio(
    "[laughs] Hello! [clears throat] This is Bark TTS. [sighs]"
)
write_wav("output.wav", SAMPLE_RATE, audio_array)

Kokoro

Kokoro is a lightweight, fast TTS model optimized for English. Despite its small size (~82M parameters), it delivers surprisingly high quality.

Philosophy: Small model, big quality, runs anywhere.

from kokoro import KPipeline
import soundfile as sf

pipeline = KPipeline(lang_code='a')  # 'a' = American English

generator = pipeline(
    "The quick brown fox jumps over the lazy dog.",
    voice='af_heart',  # pre-built voice
    speed=1.0,
)

for _, _, audio in generator:
    sf.write('output.wav', audio, 24000)

Fish Speech

Fish Audio's Fish Speech is a production-grade TTS with exceptional voice cloning from short clips. It uses a novel codec + language model architecture.

Philosophy: Production quality, fast inference, excellent cloning.

# Fish Speech via HTTP API
import requests

response = requests.post(
    "http://localhost:8080/v1/tts",
    json={
        "text": "Hello, this is Fish Speech generating audio.",
        "reference_id": "your-voice-id",
        "format": "wav",
    }
)

with open("output.wav", "wb") as f:
    f.write(response.content)

MeloTTS

MyShell's MeloTTS is ultra-fast, multi-accent TTS optimized for real-time applications. It runs efficiently on CPU and supports multiple English accents and Asian languages.

Philosophy: Real-time speed at any scale.

from melo.api import TTS

speed = 1.0
device = 'auto'

model = TTS(language='EN', device=device)
speaker_ids = model.hps.data.spk2id

output_path = 'output.wav'
model.tts_to_file(
    "Hello world! MeloTTS is very fast.",
    speaker_ids['EN-Default'],
    output_path,
    speed=speed
)

---

Quality Comparison

Naturalness Scores (MOS — Mean Opinion Score, 1-5)

MOS scores are approximate values based on published papers and community evaluations. Actual quality depends heavily on text content and voice configuration.

Model	English MOS	Multilingual MOS	Expressiveness
XTTS v2	4.3	4.1	⭐⭐⭐⭐⭐
Bark	3.9	3.7	⭐⭐⭐⭐⭐ (unique)
Kokoro	4.2	N/A (EN only)	⭐⭐⭐
Fish Speech	4.4	4.2	⭐⭐⭐⭐
MeloTTS	3.8	3.6	⭐⭐

What Each Model Does Best

Model	Standout Quality Feature
XTTS v2	Near-perfect voice cloning, emotional range
Bark	Non-speech sounds, laughter, music, effects
Kokoro	Best quality-to-size ratio, natural cadence
Fish Speech	Best overall naturalness + cloning accuracy
MeloTTS	Consistent, clean output for long texts

---

Speed Benchmarks

Characters Per Second (CPU vs GPU)

Test: "The quick brown fox jumps over the lazy dog. How are you today?" (60 chars)

Model	CPU Speed	GPU Speed (RTX 3080)	Real-time Factor
XTTS v2	~15 chars/s	~150 chars/s	0.3× (GPU)
Bark	~5 chars/s	~40 chars/s	0.1× (GPU)
Kokoro	~200 chars/s	~800 chars/s	5× (GPU)
Fish Speech	~80 chars/s	~500 chars/s	3× (GPU)
MeloTTS	~500 chars/s	~2000 chars/s	12× (GPU)

Real-time factor > 1.0 means faster than playback speed

Time to Generate 1 Minute of Audio

Model	CPU	RTX 3080	A100
XTTS v2	~8 min	~30s	~10s
Bark	~20 min	~3 min	~45s
Kokoro	~20s	~5s	~2s
Fish Speech	~45s	~8s	~3s
MeloTTS	~8s	~2s	<1s

For real-time applications: MeloTTS and Kokoro are the clear winners. Both can generate speech faster than playback speed even on CPU.

---

Language Support

Supported Languages

Model	Languages	Notable
XTTS v2	17	EN, ES, FR, DE, IT, PT, PL, TR, RU, NL, CS, AR, ZH, JA, HU, KO, HI
Bark	10+	EN, ZH, FR, DE, HI, IT, JA, KO, PL, PT, RU, ES, TR
Kokoro	2	English (US/UK), Japanese (limited)
Fish Speech	8	EN, ZH, JA, KO, FR, DE, AR, ES
MeloTTS	8	EN (4 accents), ES, FR, ZH, JA, KO

Language Quality Notes

Model	English	Chinese	Japanese	European
XTTS v2	Excellent	Good	Good	Excellent
Bark	Good	Fair	Fair	Good
Kokoro	Excellent	❌	Limited	❌
Fish Speech	Excellent	Best	Good	Good
MeloTTS	Good	Good	Good	Good

For Chinese TTS: Fish Speech and MeloTTS are the best open-source options. Both handle tones and characters naturally.

For multilingual applications: XTTS v2 supports the most languages with consistent quality across all of them.

---

Voice Cloning Comparison

Cloning Capabilities

Model	Reference Length	Cloning Quality	Zero-Shot
XTTS v2	3 seconds	⭐⭐⭐⭐⭐	✅
Bark	Voice presets only	⭐⭐⭐	Partial
Kokoro	Not supported	❌	❌
Fish Speech	10 seconds	⭐⭐⭐⭐⭐	✅
MeloTTS	Not supported	❌	❌

XTTS v2 Voice Cloning

from TTS.api import TTS
import torch

# Load model
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")
tts.to("cuda" if torch.cuda.is_available() else "cpu")

# Clone voice from reference (minimum 3 seconds, ideal 10-30 seconds)
tts.tts_to_file(
    text="""
    Welcome to our podcast. Today we're discussing the future of AI 
    and its impact on society. I'm your host, and I'm excited to 
    share some fascinating insights with you.
    """,
    speaker_wav="speaker_sample.wav",  # Your reference audio
    language="en",
    file_path="cloned_voice_output.wav"
)

Fish Speech Voice Cloning

# Clone from reference audio
fish_speech_cli tts \
  --text "This is my cloned voice speaking a new sentence." \
  --reference-audio speaker_sample.wav \
  --reference-text "The original text spoken in the reference audio." \
  --output cloned_output.wav

Bark Voice Presets

from bark import generate_audio, SAMPLE_RATE
from scipy.io.wavfile import write

# Bark uses predefined speaker codes
voice_presets = {
    "male_US": "v2/en_speaker_6",
    "female_US": "v2/en_speaker_9",
    "male_UK": "v2/en_speaker_0",
    "announcer": "v2/en_speaker_2",
}

audio = generate_audio(
    "Welcome! [laughs] This is absolutely fascinating technology.",
    history_prompt=voice_presets["female_US"]
)
write("bark_output.wav", SAMPLE_RATE, audio)

---

XTTS v2: Deep Dive

Architecture

• VITS + GPT hybrid architecture

• Trained on 16K+ hours across 17 languages

• 3-second minimum for zero-shot cloning

Installation on Clore.ai

pip install TTS
# GPU version
pip install TTS[all]

Docker Deployment

FROM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04

RUN apt-get update && apt-get install -y python3 python3-pip git ffmpeg
RUN pip3 install TTS fastapi uvicorn

WORKDIR /app
COPY server.py .

EXPOSE 5002
CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "5002"]

# server.py — XTTS v2 REST API
from fastapi import FastAPI, UploadFile, Form
from fastapi.responses import FileResponse
from TTS.api import TTS
import tempfile, os

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

@app.post("/tts")
async def synthesize(
    text: str = Form(...),
    language: str = Form("en"),
    speaker_file: UploadFile = None
):
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as out:
        output_path = out.name

    speaker_path = None
    if speaker_file:
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as ref:
            ref.write(await speaker_file.read())
            speaker_path = ref.name

    tts.tts_to_file(
        text=text,
        speaker_wav=speaker_path,
        language=language,
        file_path=output_path
    )
    return FileResponse(output_path, media_type="audio/wav")

docker build -t xtts-server .
docker run -d --gpus all -p 5002:5002 xtts-server

Weaknesses: CPML license (non-commercial without permission), slower than Kokoro/MeloTTS

---

Bark: Deep Dive

Architecture

• GPT-style transformer for audio token generation

• Three-stage process: text → semantic → coarse → fine tokens

• Generates actual audio codec tokens (EnCodec)

What Makes Bark Unique

Bark is the only open-source TTS that natively generates:

• 🎵 Background music within speech

• 😂 Laughter, sighs, throat-clearing

• 🎭 Multiple speakers in one generation

• 🌍 Mixed-language utterances

Markup Language

from bark import generate_audio, SAMPLE_RATE
from scipy.io.wavfile import write

# Special tokens for expressiveness
text = """
[clears throat] Good morning everyone. [laughs] 
Today's presentation will cover... 
[sighs deeply] ...actually quite a lot of ground.
[music: upbeat jazz] Let's get started!
"""

audio = generate_audio(text, history_prompt="v2/en_speaker_6")
write("output.wav", SAMPLE_RATE, audio)

Installation

pip install git+https://github.com/suno-ai/bark.git

Weaknesses: Slow (3-stage pipeline), inconsistent across runs, no true voice cloning

---

Kokoro: Deep Dive

Architecture

• 82M parameter StyleTTS2-based model

• Extremely small but surprisingly high quality

• Fast inference on CPU and GPU

Voices Available

from kokoro import KPipeline

pipeline = KPipeline(lang_code='a')  # 'a' = American English, 'b' = British

# Available voices
voices = {
    'af_heart': 'American Female (warm)',
    'af_bella': 'American Female (bella)',
    'af_nicole': 'American Female (nicole)',
    'am_michael': 'American Male (michael)',
    'am_fenrir': 'American Male (fenrir)',
    'bf_emma': 'British Female (emma)',
    'bm_george': 'British Male (george)',
}

# Generate with different voices
for voice_name, description in voices.items():
    gen = pipeline("Hello, this is a test.", voice=voice_name)
    for _, _, audio in gen:
        print(f"Generated with {description}")

Streaming Support

import sounddevice as sd
from kokoro import KPipeline

pipeline = KPipeline(lang_code='a')

# Stream audio in real-time as it generates
text = "This is a very long text that will be streamed as it generates, providing low-latency audio output."

for _, _, audio in pipeline(text, voice='af_heart'):
    sd.play(audio, samplerate=24000)
    sd.wait()

Weaknesses: English only (primarily), no voice cloning, limited expressiveness

---

Fish Speech: Deep Dive

Architecture

• VQGAN + Language Model architecture

• Trained on 700K+ hours of audio

• Strong multilingual with Asian language support

Installation

pip install fish-speech

# Or via Docker
docker run -d \
  --gpus all \
  -p 8080:8080 \
  fishaudio/fish-speech:latest \
  +api_server.workers_count=1

Python API

import httpx
import base64

# Via HTTP API
with httpx.Client() as client:
    response = client.post(
        "http://localhost:8080/v1/tts",
        json={
            "text": "Hello from Fish Speech! This sounds very natural.",
            "format": "wav",
            "mp3_bitrate": 128,
            "normalize": True,
        }
    )
    
    with open("fish_output.wav", "wb") as f:
        f.write(response.content)

Voice Cloning

# Upload reference, get back voice ID
with open("my_voice.wav", "rb") as f:
    response = httpx.post(
        "http://localhost:8080/v1/voices",
        files={"file": f},
        data={"text": "The text spoken in this recording."}
    )
    voice_id = response.json()["id"]

# Use cloned voice
response = httpx.post(
    "http://localhost:8080/v1/tts",
    json={
        "text": "Now speaking in the cloned voice.",
        "reference_id": voice_id,
    }
)

Weaknesses: CC BY-NC-SA license (non-commercial), higher VRAM for best quality

---

MeloTTS: Deep Dive

Architecture

• VITS2-based architecture

• Multi-accent English training

• Extremely optimized for inference speed

Accents and Languages

from melo.api import TTS

# Supported language codes and accents
configs = {
    'EN':    ['EN-Default', 'EN-US', 'EN-BR', 'EN-INDIA', 'EN-AU'],
    'ES':    ['ES'],
    'FR':    ['FR'],
    'ZH':    ['ZH'],
    'JP':    ['JP'],
    'KR':    ['KR'],
}

model = TTS(language='EN', device='cuda')
speaker_ids = model.hps.data.spk2id

# Generate with British accent
model.tts_to_file(
    "Cheerio! Fancy a spot of tea?",
    speaker_ids['EN-BR'],
    'british.wav'
)

# Generate with Indian accent
model.tts_to_file(
    "Namaste! Welcome to our company.",
    speaker_ids['EN-INDIA'],
    'indian.wav'
)

Batch Processing (Very Fast)

from melo.api import TTS
import time

model = TTS(language='EN', device='cuda')
sid = model.hps.data.spk2id['EN-Default']

texts = [
    "First sentence to synthesize.",
    "Second sentence goes here.",
    "Third and final sentence.",
]

start = time.time()
for i, text in enumerate(texts):
    model.tts_to_file(text, sid, f'output_{i}.wav')
elapsed = time.time() - start
print(f"Generated {len(texts)} files in {elapsed:.2f}s")

Weaknesses: No voice cloning, robotic at high speed, limited expressiveness

---

Deployment on Clore.ai

All-in-One TTS Server

# docker-compose.yml — TTS service with multiple backends
version: "3.8"

services:
  xtts:
    build:
      context: ./xtts
    ports:
      - "5002:5002"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    volumes:
      - ./voices:/app/voices

  kokoro:
    image: ghcr.io/remsky/kokoro-fastapi-cpu:latest
    ports:
      - "8880:8880"
    # No GPU needed!

  fish-speech:
    image: fishaudio/fish-speech:latest
    ports:
      - "8080:8080"
    command: +api_server.workers_count=2
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

VRAM Requirements Summary

Model	CPU	4GB GPU	8GB GPU	16GB GPU
XTTS v2	Slow	✅	✅	✅
Bark	Very slow	❌	✅	✅
Kokoro	Fast	✅	✅	✅
Fish Speech	Medium	✅	✅	✅
MeloTTS	Very fast	✅	✅	✅

---

Integration Examples

OpenAI-Compatible API (for drop-in replacement)

# Many TTS servers offer OpenAI-compatible endpoints
# Use Kokoro FastAPI or similar

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8880/v1",  # Your TTS server
    api_key="not-needed"
)

response = client.audio.speech.create(
    model="kokoro",
    voice="af_heart",
    input="Hello world! This uses the OpenAI TTS API format.",
)
response.stream_to_file("output.mp3")

LangChain Integration

# Using TTS with LangChain for voice agents
from langchain_community.tools import Tool
from TTS.api import TTS

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

def speak(text: str) -> str:
    tts.tts_to_file(text=text, language="en", file_path="/tmp/response.wav")
    return "/tmp/response.wav"

tts_tool = Tool(
    name="text_to_speech",
    func=speak,
    description="Convert text to speech audio file"
)

---

When to Use Which

Decision Guide

Need voice cloning from short clip?
  → XTTS v2 (3s reference) or Fish Speech (10s reference)

Need real-time/fastest generation?
  → MeloTTS (CPU friendly) or Kokoro

Need expressive speech (laughter, emotion)?
  → Bark (unique non-speech sounds) or XTTS v2

Need Chinese/Japanese/Korean?
  → Fish Speech (best CJK) or MeloTTS

English only, maximum quality?
  → Kokoro (best size/quality ratio)

Need 17+ languages?
  → XTTS v2

Commercial use allowed?
  → Kokoro (Apache) or MeloTTS (MIT) or Bark (MIT)

Non-commercial research?
  → Any (XTTS v2 CPML or Fish Speech CC BY-NC-SA)

By Application Type

Application	Best Choice	Why
Audiobook generation	XTTS v2	Natural, consistent voice
Real-time chatbot	MeloTTS or Kokoro	Fastest inference
Podcast automation	XTTS v2 or Fish Speech	Best cloning
Game characters	Bark	Expressive, varied voices
Customer service	MeloTTS	Scalable, fast
Accessibility tools	Kokoro	Lightweight, free
Voice dubbing	Fish Speech	Best cloning quality
Long-form narration	XTTS v2	Consistent quality

---

License Summary

License matters for commercial use! Always check before deploying in production.

Model	License	Commercial?	Notes
XTTS v2	Coqui Public Model License	❌ Free	Requires license for commercial
Bark	MIT	✅	Free for all use
Kokoro	Apache 2.0	✅	Free for all use
Fish Speech	CC BY-NC-SA 4.0	❌	Non-commercial only
MeloTTS	MIT	✅	Free for all use

Fully open for commercial use: Bark, Kokoro, MeloTTS

---

Cost on Clore.ai

Kokoro/MeloTTS (CPU or cheap GPU):
  Cheapest server at ~$0.05/hr → ~$36/month
  Can handle 100+ concurrent requests on CPU

XTTS v2 (RTX 3080):
  ~$0.30/hr → ~$220/month
  ~500 requests/hr capacity

Fish Speech (RTX 4090):
  ~$0.60/hr → ~$440/month  
  ~1000 requests/hr capacity

---

Useful Links

• Coqui TTS (XTTS) — 35K+ stars

• Bark GitHub — 38K+ stars

• Kokoro GitHub — 12K+ stars

• Fish Speech GitHub — 14K+ stars

• MeloTTS GitHub — 15K+ stars

• TTS Arena Leaderboard

---

Summary

Model	Use When
XTTS v2	Best voice cloning (3s ref), 17 languages, non-commercial
Bark	Expressive, laughter/effects, MIT license
Kokoro	Fast, high-quality English, Apache license
Fish Speech	Best CJK, production cloning, non-commercial
MeloTTS	Fastest, real-time, multi-accent English, MIT license

For most production Clore.ai deployments:

• Real-time voice apps → MeloTTS or Kokoro (free, fast, MIT)

• Voice cloning service → XTTS v2 or Fish Speech (check licensing)

• Expressive narration → Bark or XTTS v2

---

Clore.ai GPU Recommendations

Use Case	Recommended GPU	Est. Cost on Clore.ai
Development/Testing	RTX 3090 (24GB)	$0.07–0.21/gpu/hr
Production	RTX 4090 (24GB)	$0.14–0.42/gpu/hr
Large Scale	A100 80GB	bare metal

💡 All examples in this guide can be deployed on Clore.ai GPU servers. Browse available GPUs and rent by the hour — no commitments, full root access.