Text-to-Speech
Transformers
Safetensors
English
moss_tts_delay
feature-extraction
tts
speech-synthesis
indian-english
indian-accent
voice-agent
voice-assistant
customer-support
conversational
moss-tts
lora
audio
custom_code
Eval Results (legacy)
Instructions to use IOTEverythin/roxi-tts-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IOTEverythin/roxi-tts-pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="IOTEverythin/roxi-tts-pro", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IOTEverythin/roxi-tts-pro", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
update name to roxi-tts-pro
Browse files
README.md
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- lora
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- voice-agent
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model-index:
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- name: roxi-tts-
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results:
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- task:
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type: text-to-speech
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value: 0.18
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---
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# Roxi-TTS
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A larger, higher-fidelity member of the Roxi-TTS line. This is a LoRA fine-tune of the
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1.7B MOSS-TTS-Local-Transformer on about 4 hours of a single studio speaker, producing a
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from transformers import AutoProcessor
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from moss_tts_local.modeling_moss_tts import MossTTSDelayModel
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repo = "IOTEverythin/roxi-tts-
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.bfloat16 if device == "cuda" else torch.float32
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| roxi-tts-v2 | MOSS-TTS-Nano 0.1B | 0.96 | 0.26 | speaker A, about 50 min |
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| roxi-tts-v3 | MOSS-TTS-Nano 0.1B | 0.96 | 0.29 | speaker B, about 70 min |
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| roxi-tts-v3.1 | MOSS-TTS-Nano 0.1B | 0.96 | 0.33 | speaker B, about 4 hours |
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| roxi-tts-
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## Training
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- lora
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- voice-agent
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model-index:
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- name: roxi-tts-pro
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results:
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- task:
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type: text-to-speech
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value: 0.18
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---
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# Roxi-TTS Pro (1.7B), Indian-English voice
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A larger, higher-fidelity member of the Roxi-TTS line. This is a LoRA fine-tune of the
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1.7B MOSS-TTS-Local-Transformer on about 4 hours of a single studio speaker, producing a
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from transformers import AutoProcessor
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from moss_tts_local.modeling_moss_tts import MossTTSDelayModel
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repo = "IOTEverythin/roxi-tts-pro"
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.bfloat16 if device == "cuda" else torch.float32
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| roxi-tts-v2 | MOSS-TTS-Nano 0.1B | 0.96 | 0.26 | speaker A, about 50 min |
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| roxi-tts-v3 | MOSS-TTS-Nano 0.1B | 0.96 | 0.29 | speaker B, about 70 min |
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| roxi-tts-v3.1 | MOSS-TTS-Nano 0.1B | 0.96 | 0.33 | speaker B, about 4 hours |
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| roxi-tts-pro (this) | MOSS-TTS-Local 1.7B | 0.97 | 0.18 | speaker B, about 4 hours, most intelligible |
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## Training
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