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Alexander Veysov
3a25110cf9 Update README.md 2024-09-24 15:13:34 +03:00
Dimitrii Voronin
2fff4b8ce8 Merge pull request #541 from snakers4/adamnsandle-1
Update README.md
2024-09-24 14:48:51 +03:00
Dimitrii Voronin
64b863d2ff Update README.md 2024-09-24 14:48:35 +03:00
Dimitrii Voronin
8a3600665b Merge pull request #540 from snakers4/adamnsandle-patch-2
Update README.md
2024-09-24 13:45:31 +03:00

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@@ -32,17 +32,28 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
<details> <details>
<summary>Dependencies</summary> <summary>Dependencies</summary>
**Silero VAD uses torchaudio library for audio file I/O functionalities, which are torchaudio.info, torchaudio.load, and torchaudio.save, so a proper audio backend is required:** System requirements to run python examples on `x86-64` systems:
- Option №1 - [**FFmpeg**](https://www.ffmpeg.org/) backend. `conda install -c conda-forge 'ffmpeg<7'` - `python 3.8+`;
- Option №2 - [**sox_io**](https://pypi.org/project/sox/) backend. `apt-get install sox`, TorchAudio is tested on libsox 14.4.2. - 1G+ RAM;
- Option №3 - [**soundfile**](https://pypi.org/project/soundfile/) backend. `pip install soundfile` - A modern CPU with AVX, AVX2, AVX-512 or AMX instruction sets.
**Additional dependencies:** Dependencies:
- **torch>=1.12.0** - `torch>=1.12.0`;
- **torchaudio>=0.12.0** (for I/O functionalities only) - `torchaudio>=0.12.0` (for I/O functionalities only);
- **onnxruntime>=1.16.1** (for ONNX model usage) - `onnxruntime>=1.16.1` (for ONNX model usage).
Silero VAD uses torchaudio library for audio I/O (`torchaudio.info`, `torchaudio.load`, and `torchaudio.save`), so a proper audio backend is required:
- Option №1 - [**FFmpeg**](https://www.ffmpeg.org/) backend. `conda install -c conda-forge 'ffmpeg<7'`;
- Option №2 - [**sox_io**](https://pypi.org/project/sox/) backend. `apt-get install sox`, TorchAudio is tested on libsox 14.4.2;
- Option №3 - [**soundfile**](https://pypi.org/project/soundfile/) backend. `pip install soundfile`.
If you are planning to run the VAD using solely the `onnx-runtime`, it will run on any other system architectures where onnx-runtume is [supported](https://onnxruntime.ai/getting-started). In this case please note that:
- You will have to impolement the I/O;
- You will have to adapt the existing wrappers / examples / post-processing for your use-case.
</details> </details>