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fnlp/
MOSS-TTSD-v0.5

Name: MOSS-TTSD-v0.5
Availability: InStock

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MOSS-TTSD 🪐

Overview

MOSS-TTSD (text to spoken dialogue) is an open-source bilingual spoken dialogue synthesis model that supports both Chinese and English. It can transform dialogue scripts between two speakers into natural, expressive conversational speech. MOSS-TTSD supports voice cloning and single-session speech generation of up to 960 seconds, making it ideal for AI podcast production.

Highlights

Highly Expressive Dialogue Speech: Built on unified semantic-acoustic neural audio codec, a pre-trained large language model, millions of hours of TTS data, and 400k hours synthetic and real conversational speech, MOSS-TTSD generates highly expressive, human-like dialogue speech with natural conversational prosody.
Two-Speaker Voice Cloning: MOSS-TTSD supports zero-shot two speakers voice cloning and can generate conversational speech with accurate speaker swithcing based on dialogue scripts.
Chinese-English Bilingual Support: MOSS-TTSD enables highly expressive speech generation in both Chinese and English.
Long-Form Speech Generation (up to 960 seconds): Thanks to low-bitrate codec and training framework optimization, MOSS-TTSD has been trained for long speech generation, enabling single-session speech generation of up to 960 seconds.
Fully Open Source & Commercial-Ready: MOSS-TTSD and its future updates will be fully open-source and support free commercial use.

python

import os
import torchaudio
from transformers import AutoModel, AutoProcessor

processor = AutoProcessor.from_pretrained("fnlp/MOSS-TTSD-v0.5", codec_path="fnlp/XY_Tokenizer_TTSD_V0_hf", trust_remote_code=True)
model = AutoModel.from_pretrained("fnlp/MOSS-TTSD-v0.5", trust_remote_code=True, device_map="auto").eval()

data = [{
    "base_path": "/path/to/audio/files",
    "text": "[S1]Speaker 1 dialogue content[S2]Speaker 2 dialogue content[S1]...",
    "prompt_audio": "path/to/shared_reference_audio.wav",
    "prompt_text": "[S1]Reference text for speaker 1[S2]Reference text for speaker 2"
}]

inputs = processor(data)
token_ids = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
text, audios = processor.batch_decode(token_ids)

if not os.path.exists("outputs/"):
    os.mkdir("outputs/")
for i, data in enumerate(audios):
    for j, fragment in enumerate(data):
        torchaudio.save(f"outputs/audio_{i}_{j}.wav", fragment.cpu(), 24000)