Instructions to use onecxi/mms-telugu-female-indic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onecxi/mms-telugu-female-indic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="onecxi/mms-telugu-female-indic")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("onecxi/mms-telugu-female-indic") model = AutoModelForPreTraining.from_pretrained("onecxi/mms-telugu-female-indic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| language: | |
| - te | |
| base_model: | |
| - facebook/mms-tts | |
| pipeline_tag: text-to-speech | |
| library_name: transformers | |
| # MMS Telugu Female | |
| This model is trained using **IndicTTS dataset** with Telugu female speaker. | |
| ## Model | |
| **mms-telugu-female-indic**: **Telugu model with Telugu female speaker** from the IndicTTS dataset. | |
| ## Sample Examples | |
| | Text | Synthesized Audio | | |
| |------------|------------| | |
| | ఈ పనిని పూర్తి చేయడానికి రెండు రోజులు పడుతుంది. నేను మీకు సహాయం చేయగలను. | <audio controls src="https://huggingface.co/datasets/onecxi/cxi-sample-data/resolve/main/tts-examples/mms-telugu-female-indic/telugu_example_1.wav"></audio> | | |
| | ఈ పుస్తకం నాకు చాలా నచ్చింది. ఇందులో కొత్త విషయాలు చాలా ఉన్నాయి. | <audio controls src="https://huggingface.co/datasets/onecxi/cxi-sample-data/resolve/main/tts-examples/mms-telugu-female-indic/telugu_example_2.wav"></audio> | | |
| | నేను రేపు ఉదయం పది గంటలకు బయలుదేరుతాను. మీరు రాగలరా? | <audio controls src="https://huggingface.co/datasets/onecxi/cxi-sample-data/resolve/main/tts-examples/mms-telugu-female-indic/telugu_example_3.wav"></audio> | | |
| ## Inference | |
| To use these models for inference, you'll need to install the `transformers` and `accelerate` libraries. | |
| First, install the necessary libraries: | |
| ``` | |
| pip install --upgrade transformers accelerate | |
| ``` | |
| Then, run inference with the following code-snippet: | |
| ```python | |
| from transformers import VitsModel, AutoTokenizer | |
| import torch | |
| from IPython.display import Audio | |
| model_path = "onecxi/mms-telugu-female-indic" # This is the path to your fine-tuned model | |
| model = VitsModel.from_pretrained(model_path) | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| text = "This is a Telugu model trained using a Telugu female speaker." | |
| inputs = tokenizer(text, return_tensors="pt") | |
| with torch.no_grad(): | |
| output = model(**inputs).waveform | |
| Audio(output.numpy(), rate=model.config.sampling_rate) | |
| ``` | |
| ## Disclaimer | |
| This Text-to-Speech (TTS) model is intended solely for research and educational use. Any use of the model must comply with all applicable laws, regulations, and ethical standards. The unauthorized use of this model for impersonating real individuals without their explicit consent is strictly prohibited. | |
| Additionally, the model must not be used to create or distribute deceptive, misleading, or fraudulent content, including but not limited to fake news or scams. Any use of the model for illegal, harmful, or malicious purposes is expressly forbidden. | |
| By using this model, you acknowledge and agree to these terms. The creators and distributors of the model disclaim any liability for misuse and do not support or condone unethical or unlawful applications. | |