Documentation for denoising and segmentation (#453)

* documentation for denoising and segmentation

* addition

* typo

* del

* add

* add

README.md

@@ -115,6 +115,7 @@ expressivity_predict <path_to_input_audio> --tgt_lang <tgt_lang> --model_name se
 
 [Streaming Evaluation README](src/seamless_communication/cli/streaming) has detailed instructions for running evaluations for the SeamlessStreaming and Seamless models. The CLI has an `--no-scoring` option that can be used to skip the scoring part and just run inference.
 
+Please check the inference [README](src/seamless_communication/inference) for more details.
 
 ## Running SeamlessStreaming Demo
 You can duplicate the [SeamlessStreaming HF space](https://huggingface.co/spaces/facebook/seamless-streaming?duplicate=true) to run the streaming demo.

src/seamless_communication/inference/README.md

@@ -0,0 +1,95 @@
+# Running inference
+
+### SeamlessM4T Inference
+Here’s an example of using the CLI from the root directory to run inference.
+
+S2ST task:
+```bash
+m4t_predict <path_to_input_audio> --task s2st --tgt_lang <tgt_lang> --output_path <path_to_save_audio>
+```
+T2TT task:
+```bash
+m4t_predict <input_text> --task t2tt --tgt_lang <tgt_lang> --src_lang <src_lang>
+```
+Please refer to the [inference README](src/seamless_communication/cli/m4t/predict) for detailed instruction on how to run inference and the list of supported languages on the source, target sides for speech, text modalities.
+
+For running S2TT/ASR natively (without Python) using GGML, please refer to [the unity.cpp section](#unitycpp).
+
+# Transcription Utilities: Denoising and Segmentation 
+
+The following information shows how to use denoising and segmentation tools for noisy and long input audios.
+
+## Demucs: Audio Denoising Tool
+
+The 'Demucs' class provides functionality for denoising audio in the transcription pipeline. It supports various configuration options, allowing for fine-tuning denoising performance based on specific requirements. 
+
+Key Features:
+
+- Denoising audio using the Demucs model.
+- Configurable parameters for denoising.
+- Support for both Tensor input and audio file input.
+- Automatic cleanup of temporary files generated during denoising.
+
+### Installation
+
+Manually install demucs: 
+
+pip install git+https://github.com/facebookresearch/demucs#egg=demucs
+
+### Usage
+
+To utilize Demucs for denoising audio, instantiate the Transcriber class and optionally the DenoisingConfig class with desired configuration. 'denoise' parameter is False by default, and needs to be set to True to use denoising.
+
+```
+import torch
+from seamless_communication.inference import Transcriber
+from seamless_communication.denoise.demucs import DenoisingConfig
+
+model_name = "seamlessM4T_v2_large"
+vocoder_name = "vocoder_v2" if model_name == "seamlessM4T_v2_large" else "vocoder_36langs"
+
+transcriber = Transcriber (
+    model_name,
+    device=torch.device("cpu"),
+    dtype=torch.float32,
+)
+
+denoise_config = DenoisingConfig(float32= True)
+
+txt = transcriber.transcribe(audio="example.wav", src_lang="eng", denoise=True, denoise_config=denoise_config)
+```
+
+## Silero VAD Segmenter: Audio Segmentation Tool
+
+The 'SileroVADSegmenter' class offers functionality for segmenting long audio recordings into chunks in the transcription pipeline. This tool segments based on speech timestamps. 
+
+Key Features:
+
+- Segmenting long audio recordings into chunks based on speech presence.
+- Automatic segmenting of all audio longer than the chunk size. 
+- Configurable parameters for chunk size and pause length.
+- Resampling audio to match the model's sample rate.
+- Efficient speech probability computation using sliding windows.
+
+### Usage
+
+To utilize Silero VAD for segmenting audio, instantiate the Transcriber class. When using the transcribe method, audio will be segmented automatically if it is longer than chunk_size_sec, which has a default value of 20. Use a smaller value for better quality transcription.
+pause_length_sec determines the duration of silence between segments and has a default value of 1 second. This parameter can be customized.
+
+```
+import torch
+from seamless_communication.inference import Transcriber
+
+model_name = "seamlessM4T_v2_large"
+vocoder_name = "vocoder_v2" if model_name == "seamlessM4T_v2_large" else "vocoder_36langs"
+
+transcriber = Transcriber (
+    model_name,
+    device=torch.device("cpu"),
+    dtype=torch.float32,
+)
+
+input_audio = "example.wav"
+
+txt = transcriber.transcribe(audio=input_audio, src_lang="eng", chunk_size_sec=10, pause_length_sec=.5)
+```