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  1. .gitignore +160 -0
  2. LICENSE +201 -0
  3. README.md +173 -12
  4. app.py +80 -0
  5. requirements.txt +5 -0
  6. test.py +23 -0
.gitignore ADDED
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ # C extensions
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+ *.so
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ lib/
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+ lib64/
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+ sdist/
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+ var/
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+ wheels/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Usually these files are written by a python script from a template
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+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
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+ # Unit test / coverage reports
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+ htmlcov/
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+ ipython_config.py
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+ # pyenv
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+ # For a library or package, you might want to ignore these files since the code is
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+ # and can be added to the global gitignore or merged into this file. For a more nuclear
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+ #.idea/
LICENSE ADDED
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README.md CHANGED
@@ -1,12 +1,173 @@
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- ---
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- title: Sarvm Audio Translation
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- emoji: 🏢
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- colorFrom: indigo
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- colorTo: green
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- sdk: streamlit
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- sdk_version: 1.30.0
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- app_file: app.py
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- pinned: false
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Seamless Translation
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+
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+ Seamless-m4t-v2-large is a multimodal speech detection and translation model.
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+
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+ More information is available on the model [card](https://huggingface.co/facebook/seamless-m4t-v2-large)
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+
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+ Google Colab available [here](https://colab.research.google.com/drive/1ndSYhHxPkMl58EpkZ5pWrfsTxYgMhwWO?usp=sharing)
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+
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+ ![header](assets/header.png)
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+
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+ ![Python](https://img.shields.io/badge/Python-3.8-3776AB?style=flat&logo=python&logoColor=white)
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+ ![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=flat&logo=Streamlit&logoColor=white)
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+ ![GitHub](https://img.shields.io/badge/GitHub-100000?style=flat&logo=github&logoColor=white)
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+ ![Hugging Face](https://img.shields.io/badge/Hugging%20Face-FFEF00?style=flat&logo=HuggingFace&logoColor=black)
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+
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+
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+ ## Installation
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+
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+ To install Seamless Translation, follow these steps:
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+
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+ ```bash
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+ git clone
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+ cd seamless-translation
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+ pip install -r requirements.txt
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+ ```
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+
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+ ## Usage
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+
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+ After installation, you can easily run the application using Streamlit. Use the following command to start the application:
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+
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+ ```bash
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+ streamlit run app.py
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+ ```
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+
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+ ## Features
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+
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+ Seamless Translation offers a comprehensive range of features:
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+
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+ + Multilingual Support: It supports an extensive list of languages and dialects, with capabilities for both speech and text translation.
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+ + Speech and Text Input: The model can process both spoken words (Sp) and textual data (Tx), offering flexibility in usage.
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+ + Diverse Script Handling: It accommodates a wide array of scripts, including Latin, Cyrillic, Arabic, and more, ensuring accurate translation across different writing systems.
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+ + Seamless Communication: Designed for both source and target language translation, the model provides a smooth and efficient translation process.
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+
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+ ## Testing
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+
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+ Test the model and cache the download to make the streamlit script startup faster:
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+
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+ ```
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+ python test.py
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+ ```
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+
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+ ### Supported Languages
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+
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+ | code | language | script | Source | Target |
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+ | ---- | ---------------------- | ---------- | ------ | ------ |
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+ | afr | Afrikaans | Latn | Sp, Tx | Tx |
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+ | amh | Amharic | Ethi | Sp, Tx | Tx |
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+ | arb | Modern Standard Arabic | Arab | Sp, Tx | Sp, Tx |
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+ | ary | Moroccan Arabic | Arab | Sp, Tx | Tx |
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+ | arz | Egyptian Arabic | Arab | Sp, Tx | Tx |
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+ | asm | Assamese | Beng | Sp, Tx | Tx |
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+ | ast | Asturian | Latn | Sp | \-- |
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+ | azj | North Azerbaijani | Latn | Sp, Tx | Tx |
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+ | bel | Belarusian | Cyrl | Sp, Tx | Tx |
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+ | ben | Bengali | Beng | Sp, Tx | Sp, Tx |
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+ | bos | Bosnian | Latn | Sp, Tx | Tx |
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+ | bul | Bulgarian | Cyrl | Sp, Tx | Tx |
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+ | cat | Catalan | Latn | Sp, Tx | Sp, Tx |
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+ | ceb | Cebuano | Latn | Sp, Tx | Tx |
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+ | ces | Czech | Latn | Sp, Tx | Sp, Tx |
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+ | ckb | Central Kurdish | Arab | Sp, Tx | Tx |
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+ | cmn | Mandarin Chinese | Hans | Sp, Tx | Sp, Tx |
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+ | cmn_Hant | Mandarin Chinese | Hant | Sp, Tx | Sp, Tx |
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+ | cym | Welsh | Latn | Sp, Tx | Sp, Tx |
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+ | dan | Danish | Latn | Sp, Tx | Sp, Tx |
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+ | deu | German | Latn | Sp, Tx | Sp, Tx |
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+ | ell | Greek | Grek | Sp, Tx | Tx |
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+ | eng | English | Latn | Sp, Tx | Sp, Tx |
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+ | est | Estonian | Latn | Sp, Tx | Sp, Tx |
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+ | eus | Basque | Latn | Sp, Tx | Tx |
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+ | fin | Finnish | Latn | Sp, Tx | Sp, Tx |
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+ | fra | French | Latn | Sp, Tx | Sp, Tx |
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+ | fuv | Nigerian Fulfulde | Latn | Sp, Tx | Tx |
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+ | gaz | West Central Oromo | Latn | Sp, Tx | Tx |
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+ | gle | Irish | Latn | Sp, Tx | Tx |
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+ | glg | Galician | Latn | Sp, Tx | Tx |
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+ | guj | Gujarati | Gujr | Sp, Tx | Tx |
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+ | heb | Hebrew | Hebr | Sp, Tx | Tx |
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+ | hin | Hindi | Deva | Sp, Tx | Sp, Tx |
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+ | hrv | Croatian | Latn | Sp, Tx | Tx |
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+ | hun | Hungarian | Latn | Sp, Tx | Tx |
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+ | hye | Armenian | Armn | Sp, Tx | Tx |
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+ | ibo | Igbo | Latn | Sp, Tx | Tx |
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+ | ind | Indonesian | Latn | Sp, Tx | Sp, Tx |
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+ | isl | Icelandic | Latn | Sp, Tx | Tx |
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+ | ita | Italian | Latn | Sp, Tx | Sp, Tx |
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+ | jav | Javanese | Latn | Sp, Tx | Tx |
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+ | jpn | Japanese | Jpan | Sp, Tx | Sp, Tx |
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+ | kam | Kamba | Latn | Sp | \-- |
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+ | kan | Kannada | Knda | Sp, Tx | Tx |
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+ | kat | Georgian | Geor | Sp, Tx | Tx |
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+ | kaz | Kazakh | Cyrl | Sp, Tx | Tx |
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+ | kea | Kabuverdianu | Latn | Sp | \-- |
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+ | khk | Halh Mongolian | Cyrl | Sp, Tx | Tx |
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+ | khm | Khmer | Khmr | Sp, Tx | Tx |
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+ | kir | Kyrgyz | Cyrl | Sp, Tx | Tx |
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+ | kor | Korean | Kore | Sp, Tx | Sp, Tx |
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+ | lao | Lao | Laoo | Sp, Tx | Tx |
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+ | lit | Lithuanian | Latn | Sp, Tx | Tx |
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+ | ltz | Luxembourgish | Latn | Sp | \-- |
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+ | lug | Ganda | Latn | Sp, Tx | Tx |
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+ | luo | Luo | Latn | Sp, Tx | Tx |
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+ | lvs | Standard Latvian | Latn | Sp, Tx | Tx |
114
+ | mai | Maithili | Deva | Sp, Tx | Tx |
115
+ | mal | Malayalam | Mlym | Sp, Tx | Tx |
116
+ | mar | Marathi | Deva | Sp, Tx | Tx |
117
+ | mkd | Macedonian | Cyrl | Sp, Tx | Tx |
118
+ | mlt | Maltese | Latn | Sp, Tx | Sp, Tx |
119
+ | mni | Meitei | Beng | Sp, Tx | Tx |
120
+ | mya | Burmese | Mymr | Sp, Tx | Tx |
121
+ | nld | Dutch | Latn | Sp, Tx | Sp, Tx |
122
+ | nno | Norwegian Nynorsk | Latn | Sp, Tx | Tx |
123
+ | nob | Norwegian Bokmål | Latn | Sp, Tx | Tx |
124
+ | npi | Nepali | Deva | Sp, Tx | Tx |
125
+ | nya | Nyanja | Latn | Sp, Tx | Tx |
126
+ | oci | Occitan | Latn | Sp | \-- |
127
+ | ory | Odia | Orya | Sp, Tx | Tx |
128
+ | pan | Punjabi | Guru | Sp, Tx | Tx |
129
+ | pbt | Southern Pashto | Arab | Sp, Tx | Tx |
130
+ | pes | Western Persian | Arab | Sp, Tx | Sp, Tx |
131
+ | pol | Polish | Latn | Sp, Tx | Sp, Tx |
132
+ | por | Portuguese | Latn | Sp, Tx | Sp, Tx |
133
+ | ron | Romanian | Latn | Sp, Tx | Sp, Tx |
134
+ | rus | Russian | Cyrl | Sp, Tx | Sp, Tx |
135
+ | slk | Slovak | Latn | Sp, Tx | Sp, Tx |
136
+ | slv | Slovenian | Latn | Sp, Tx | Tx |
137
+ | sna | Shona | Latn | Sp, Tx | Tx |
138
+ | snd | Sindhi | Arab | Sp, Tx | Tx |
139
+ | som | Somali | Latn | Sp, Tx | Tx |
140
+ | spa | Spanish | Latn | Sp, Tx | Sp, Tx |
141
+ | srp | Serbian | Cyrl | Sp, Tx | Tx |
142
+ | swe | Swedish | Latn | Sp, Tx | Sp, Tx |
143
+ | swh | Swahili | Latn | Sp, Tx | Sp, Tx |
144
+ | tam | Tamil | Taml | Sp, Tx | Tx |
145
+ | tel | Telugu | Telu | Sp, Tx | Sp, Tx |
146
+ | tgk | Tajik | Cyrl | Sp, Tx | Tx |
147
+ | tgl | Tagalog | Latn | Sp, Tx | Sp, Tx |
148
+ | tha | Thai | Thai | Sp, Tx | Sp, Tx |
149
+ | tur | Turkish | Latn | Sp, Tx | Sp, Tx |
150
+ | ukr | Ukrainian | Cyrl | Sp, Tx | Sp, Tx |
151
+ | urd | Urdu | Arab | Sp, Tx | Sp, Tx |
152
+ | uzn | Northern Uzbek | Latn | Sp, Tx | Sp, Tx |
153
+ | vie | Vietnamese | Latn | Sp, Tx | Sp, Tx |
154
+ | xho | Xhosa | Latn | Sp | \-- |
155
+ | yor | Yoruba | Latn | Sp, Tx | Tx |
156
+ | yue | Cantonese | Hant | Sp, Tx | Tx |
157
+ | zlm | Colloquial Malay | Latn | Sp | \-- |
158
+ | zsm | Standard Malay | Latn | Tx | Tx |
159
+ | zul | Zulu | Latn | Sp, Tx | Tx |
160
+
161
+
162
+ ### Citations
163
+
164
+
165
+ ```bibtex
166
+ @inproceedings{seamless2023,
167
+ title="Seamless: Multilingual Expressive and Streaming Speech Translation",
168
+ author="{Seamless Communication}, Lo{\"i}c Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-juss{\`a}, Maha Elbayad, Hongyu Gong, Francisco Guzm{\'a}n, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson",
169
+ journal={ArXiv},
170
+ year={2023}
171
+ }
172
+
173
+ ```
app.py ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ from transformers import AutoProcessor, SeamlessM4Tv2Model
3
+ import torchaudio
4
+ import soundfile as sf
5
+ import torch
6
+ import os
7
+
8
+ language_map = {
9
+ "Modern Standard Arabic": "arb", "Bengali": "ben", "Catalan": "cat",
10
+ "Czech": "ces", "Mandarin Chinese": "cmn", "Welsh": "cym",
11
+ "Danish": "dan", "German": "deu", "English": "eng",
12
+ "Estonian": "est", "Finnish": "fin", "French": "fra",
13
+ "Hindi": "hin", "Indonesian": "ind", "Italian": "ita",
14
+ "Japanese": "jpn", "Kannada": "kan", "Korean": "kor",
15
+ "Maltese": "mlt", "Dutch": "nld", "Western Persian": "pes",
16
+ "Polish": "pol", "Portuguese": "por", "Romanian": "ron",
17
+ "Russian": "rus", "Slovak": "slk", "Spanish": "spa",
18
+ "Swedish": "swe", "Swahili": "swh", "Tamil": "tam",
19
+ "Telugu": "tel", "Tagalog": "tgl", "Thai": "tha",
20
+ "Turkish": "tur", "Ukrainian": "ukr", "Urdu": "urd",
21
+ "Northern Uzbek": "uzn", "Vietnamese": "vie"
22
+ }
23
+
24
+ # Check if CUDA (GPU support) is available and set the device accordingly
25
+ device = "cuda" if torch.cuda.is_available() else "cpu"
26
+
27
+ # Function to load and cache the model and processor
28
+ @st.cache_resource
29
+ def load_model_and_processor():
30
+ processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
31
+ model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
32
+ model.to(device)
33
+ return processor, model
34
+
35
+ processor, model = load_model_and_processor()
36
+
37
+ # Streamlit app layout
38
+ st.title("Seamless Translation")
39
+
40
+ # Sidebar components
41
+ st.sidebar.header("Input Settings")
42
+ input_text = st.sidebar.text_input("Enter text for conversion:", "Hello, my dog is cute")
43
+ selected_language_name = st.sidebar.selectbox("Select Target Language:", list(language_map.keys()))
44
+ selected_language_code = language_map[selected_language_name]
45
+
46
+ # Function to convert text to audio and save
47
+ def text_to_audio(text, language):
48
+ text_inputs = processor(text=text, src_lang="eng", return_tensors="pt").to(device)
49
+ audio_array = model.generate(**text_inputs, tgt_lang=language)[0].cpu().numpy().squeeze()
50
+ file_path = 'audio_from_text.wav'
51
+ sf.write(file_path, audio_array, 16000)
52
+ return file_path
53
+
54
+
55
+ # Function to convert audio to audio and save
56
+ def audio_to_audio(input_audio_path, language):
57
+ audio, orig_freq = torchaudio.load(input_audio_path)
58
+ audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000).to(device)
59
+ audio_inputs = processor(audios=audio, return_tensors="pt").to(device)
60
+ audio_array = model.generate(**audio_inputs, tgt_lang=language)[0].cpu().numpy().squeeze()
61
+ file_path = 'audio_from_audio.wav'
62
+ sf.write(file_path, audio_array, 16000)
63
+ return file_path
64
+
65
+ # UI to trigger text-to-audio conversion
66
+ if st.sidebar.button("Convert Text to Audio"):
67
+ with st.spinner("Converting..."):
68
+ audio_path = text_to_audio(input_text, selected_language_code)
69
+ st.audio(audio_path)
70
+
71
+ # UI to upload audio file and trigger audio-to-audio conversion
72
+ uploaded_audio = st.sidebar.file_uploader("Upload audio for conversion:", type=["wav"])
73
+ if uploaded_audio is not None and st.button("Convert Uploaded Audio"):
74
+ with st.spinner("Converting..."):
75
+ audio_file_path = f"temp_{uploaded_audio.name}"
76
+ with open(audio_file_path, "wb") as f:
77
+ f.write(uploaded_audio.getvalue())
78
+ converted_audio_path = audio_to_audio(audio_file_path, selected_language_code)
79
+ st.audio(converted_audio_path)
80
+ os.remove(audio_file_path)
requirements.txt ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ torch
2
+ torchaudio
3
+ soundfile
4
+ sentencepiece
5
+ git+https://github.com/huggingface/transformers.git
test.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import AutoProcessor, SeamlessM4Tv2Model
2
+ import torchaudio
3
+ import soundfile as sf
4
+
5
+ # Initialize the processor and model
6
+ processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
7
+ model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
8
+
9
+ # Text to audio conversion
10
+ text_inputs = processor(text="Hello, my dog is cute", src_lang="eng", return_tensors="pt")
11
+ audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
12
+
13
+ # Save the audio generated from text
14
+ sf.write('audio_from_text.wav', audio_array_from_text, 16000) # 16000 is the sampling rate
15
+
16
+ # Audio to audio conversion
17
+ audio, orig_freq = torchaudio.load("https://www2.cs.uic.edu/~i101/SoundFiles/preamble10.wav")
18
+ audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000) # Resample to 16 kHz
19
+ audio_inputs = processor(audios=audio, return_tensors="pt")
20
+ audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
21
+
22
+ # Save the audio generated from another audio
23
+ sf.write('audio_from_audio.wav', audio_array_from_audio, 16000) # 16000 is the sampling rate