| ---
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| license: cc-by-4.0
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| language:
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| - hi
|
| - as
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| - bn
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| - bo
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| - en
|
| - gu
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| - kn
|
| - ml
|
| - mr
|
| - or
|
| - pa
|
| - ta
|
| - te
|
| - ur
|
| tags:
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| - tts
|
| - indictts
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| - fs2
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| - mfa
|
| - HS
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| - hybrid_segmentation
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| - fastspeech2
|
| ---
|
| # Latest Fastspeech2 Models using FLAT Start
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|
|
| This repository contains new and high quality Fastspeech2 Models for Indian languages implemented using the Flat Start for speech synthesis. The models are capable of generating mel-spectrograms from text inputs and can be used to synthesize speech.
|
|
|
| The Repo is large in size. New Models are in "language"_latest folder.
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|
|
| Supported languages: Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Konkani(Maharashtrian), Maithili, Malayalam, Manipuri, Nepali, Punjabi, Rajasthani, Sanskrit, Tamil, Telugu.
|
|
|
| NOTE - I do not own any right to the repository, all the rights goes to original owner. This repository is meant to help easy installation of the speech models.
|
|
|
| ## Model Files
|
|
|
| The model for each language includes the following files:
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|
|
| - `config.yaml`: Configuration file for the Fastspeech2 Model.
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| - `energy_stats.npz`: Energy statistics for normalization during synthesis.
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| - `feats_stats.npz`: Features statistics for normalization during synthesis.
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| - `feats_type`: Features type information.
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| - `pitch_stats.npz`: Pitch statistics for normalization during synthesis.
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| - `model.pth`: Pre-trained Fastspeech2 model weights.
|
|
|
| ## Installation
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|
|
| 1. Install [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/) first. Create a conda environment using the provided `environment.yml` file:
|
|
|
| ```shell
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| conda env create -f environment.yml
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| ```
|
|
|
| 2.Activate the conda environment (check inside environment.yaml file):
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| ```shell
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| conda activate tts-hs-hifigan
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| ```
|
|
|
| 3. Install PyTorch separately (you can install the specific version based on your requirements):
|
| ```shell
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| conda install pytorch cudatoolkit
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| pip install torchaudio
|
| ```
|
| ## Vocoder
|
| For generating WAV files from mel-spectrograms, you can use a vocoder of your choice. One popular option is the [HIFIGAN](https://github.com/jik876/hifi-gan) vocoder (Clone this repo and put it in the current working directory). Please refer to the documentation of the vocoder you choose for installation and usage instructions.
|
|
|
| (**We have used the HIFIGAN V1 vocoder and have provided Vocoder for few languages in the Vocoder folder. If needed, make sure to adjust the path in the inference file.**)
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|
|
| ## Usage
|
|
|
| The directory paths are Relative. ( But if needed, Make changes to **text_preprocess_for_inference.py** and **inference.py** file, Update folder/file paths wherever required.)
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|
|
| **Please give language/gender in small cases and sample text between quotes. Adjust output speed using the alpha parameter (higher for slow voiced output and vice versa). Output argument is optional; the provide name will be used for the output file.**
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|
|
| Use the inference file to synthesize speech from text inputs:
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| ```shell
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| python inference.py --sample_text "Your input text here" --language <language>_latest --gender <gender> --alpha <alpha> --output_file <file_name.wav OR path/to/file_name.wav>
|
| ```
|
|
|
| **Example:**
|
|
|
| ```
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| python inference.py --sample_text "श्रीलंका और पाकिस्तान में खेला जा रहा एशिया कप अब तक का सबसे विवादित टूर्नामेंट होता जा रहा है।" --language hindi_latest --gender male --alpha 1 --output_file male_hindi_output.wav
|
| ```
|
| The file will be stored as `male_hindi_output.wav` and will be inside current working directory. If **--output_file** argument is not given it will be stored as `<language>_<gender>_output.wav` in the current working directory.
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|
|
| **Use "language"_latest in --language to use latest models.**
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|
|
|
|
| ### Citation
|
| If you use this Fastspeech2 Model in your research or work, please consider citing:
|
|
|
| “
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| COPYRIGHT
|
| 2025, Speech Technology Consortium,
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|
|
| Bhashini, MeiTY and by Hema A Murthy & S Umesh,
|
|
|
|
|
| DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
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| and
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| ELECTRICAL ENGINEERING,
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| IIT MADRAS. ALL RIGHTS RESERVED "
|
|
|
|
|
|
|
| Shield: [![CC BY 4.0][cc-by-shield]][cc-by]
|
|
|
| This work is licensed under a
|
| [Creative Commons Attribution 4.0 International License][cc-by].
|
|
|
| [![CC BY 4.0][cc-by-image]][cc-by]
|
|
|
| [cc-by]: http://creativecommons.org/licenses/by/4.0/
|
| [cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
|
| [cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg |
