Text-to-Speech
Transformers
Safetensors
English
moss_tts_nano
feature-extraction
tts
moss-tts-nano
indian-english
lora
voice-cloning
custom_code
Instructions to use IOTEverythin/roxi-tts-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IOTEverythin/roxi-tts-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="IOTEverythin/roxi-tts-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("IOTEverythin/roxi-tts-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: works on modern transformers now; soften version note
Browse files
README.md
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@@ -37,19 +37,19 @@ A LoRA fine-tune of [**MOSS-TTS-Nano**](https://huggingface.co/OpenMOSS-Team/MOS
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The voice became the target Indian-English speaker **without** a reference clip, with intelligibility unchanged.
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##
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This
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`TypeError: unsupported operand type(s) for |: 'list' and 'set'`
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(the LM heads are *tied* weights restored on load — a version mismatch breaks the tying). Pin:
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```bash
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#
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pip install "transformers==4.57.1" soundfile sentencepiece numpy huggingface_hub
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# CPU-only: pip install torch==2.7.0 torchaudio==2.7.0 (default index)
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```
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## Usage
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```python
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The voice became the target Indian-English speaker **without** a reference clip, with intelligibility unchanged.
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## Requirements
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This repo's custom modeling code includes a **cross-version compatibility fix**, so it loads on
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both `transformers==4.57.1` and **modern Transformers (tested 5.12.1)** — the older
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`TypeError: unsupported operand type(s) for |: 'list' and 'set'` is resolved. Install:
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```bash
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pip install transformers torch torchaudio soundfile sentencepiece numpy huggingface_hub
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# GPU (Blackwell/most NVIDIA), if needed:
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# pip install torch==2.7.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128
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```
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`torchaudio` is required (the modeling code imports it). The `MISSING ..._lm_head.weight` line in
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the load log is **cosmetic** — those heads are *tied* weights, rebound to the embeddings on load.
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For exact parity with the training environment you may still pin `transformers==4.57.1`.
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## Usage
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```python
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