Update README and requirements.txt: add gradio==3.39.0 and improve docs

README.md

@@ -1,96 +1,51 @@
-<<<<<<< HEAD
-SEAGAN Speech Enhancement & API
-===============================
+---
+title: SEGAN
+emoji: 🏢
+colorFrom: green
+colorTo: yellow
+sdk: gradio
+sdk_version: "6.1.0"
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Remove BackgroundNoise and Generate Image from the Audio
+---
+
+# SEAGAN Speech Enhancement & API
 
 A minimal speech-denoising project built around a SEGAN-style U-Net generator. It includes:
+
 - Training script to learn on paired noisy/clean audio.
 - Inference pipeline that denoises long clips in chunks and can pack output audio losslessly into PNG.
 - FastAPI service to expose denoise + PNG pack/restore endpoints.
+- Gradio demo for Hugging Face Spaces (app.py).
 
-Repo Contents
--------------
-- `SEGAN.py` – training components: config, dataset, U-Net generator, PatchGAN discriminator, training loop.
-- `pipeline.py` – inference utilities: chunked denoiser, spectral gating cleanup, PNG pack/restore helpers.
-- `app.py` – FastAPI app wiring the pipeline for HTTP use.
-- `seagan_final.pt` – example checkpoint (place your own if different).
-- `requirements.txt` – Python dependencies.
-
-Prerequisites
--------------
-- Python 3.9+ (tested with PyTorch CPU/GPU builds).
-- For GPU inference/training, install the matching CUDA-enabled `torch`/`torchaudio`.
-- FFmpeg is not required; `torchaudio` handles WAV I/O.
-
-Install
--------
-```bash
-python -m venv .venv
-source .venv/Scripts/activate  # on Windows PowerShell: .\.venv\Scripts\activate
-pip install -r requirements.txt
-```
-If you need a specific CUDA wheel, install torch/torchaudio first, then run `pip install -r requirements.txt` with `--no-deps`.
-
-Quick Inference (CLI)
----------------------
-Use the chunked denoiser directly:
-```bash
-python pipeline.py --input path/to/noisy.wav --output path/to/denoised.wav --checkpoint seagan_final.pt
-```
-Notes:
-- `--png-width` controls width when packing to PNG; omit `--no-pack` to also write `*_packed.png` and a reconstructed WAV check.
-- The denoiser mirrors/overlaps chunks to reduce seams and optionally runs a spectral subtraction cleanup.
+---
 
-FastAPI Service
----------------
-Environment variables:
-- `CHECKPOINT_PATH` (default `/app/checkpoints/seagan_final.pt`)
-- `CHECKPOINT_URL` (optional download at startup)
-- `SAMPLE_RATE` (default `16000`)
-- `PNG_WIDTH` (default `2048`)
+## Repo Contents
 
-Run locally:
-```bash
-uvicorn app:app --host 0.0.0.0 --port 8000
-```
+- `SEGAN.py` – training components: config, dataset, U-Net generator, PatchGAN discriminator, training loop.  
+- `pipeline.py` – inference utilities: chunked denoiser, spectral gating cleanup, PNG pack/restore helpers.  
+- `app.py` – Gradio / FastAPI app wiring the pipeline for UI/API use.  
+- `checkpoint/seagan_final.pt` – example checkpoint (place your own if different) — tracked with git-lfs.  
+- `requirements.txt` – Python dependencies.
 
-Endpoints:
-- `POST /denoise-and-pack` – form-data key `file` with WAV. Returns packed PNG of denoised audio.
-- `POST /restore-from-png` – form-data key `file` with packed PNG. Returns restored WAV.
-- `GET /health` – health check.
+---
 
-Model Training
---------------
-`SEGAN.py` trains on paired noisy/clean WAVs. Update `Config.noisy_dir`, `Config.clean_dir`, and `Config.save_dir` to your paths, then run:
-```bash
-python SEGAN.py
-```
-Checkpoints are written every 5 epochs and as `seagan_final.pt` at the end. The inference pipeline expects a `G_state` entry inside the checkpoint.
+## Prerequisites
 
-PNG Packing/Restoration Utilities
----------------------------------
-`pipeline.py` exposes:
-- `save_audio_as_png_lossless(tensor, png_path, width)` – stores int16 PCM in a lossless PNG.
-- `load_audio_from_png_lossless(png_path, original_length)` – restores the tensor.
-- `write_wav_from_tensor(tensor, out_wav_path, sr)` – writes mono WAV.
+- Python 3.9+ (tested with PyTorch CPU/GPU builds).  
+- For GPU inference/training, install the matching CUDA-enabled `torch`/`torchaudio`.  
+- FFmpeg is not required; `torchaudio` handles WAV I/O.
 
-Tips
-----
-- Keep input WAVs mono or they will be averaged to mono.
-- Large files are chunked; adjust `chunk_seconds` and `overlap` in `denoise_chunked_final`.
-- Ensure the checkpoint matches the model architecture in `SEGAN.py`.
-=======
----
-title: SEGAN
-emoji: 🏢
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.1.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Remove BackgroundNoise and Generate Image from the Audio
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
->>>>>>> b15accf770b0a139b21a8b09501ce8fd93a23c44
+## Install
+
+```bash
+python -m venv .venv
+# Windows PowerShell:
+.\.venv\Scripts\Activate.ps1
+# or cmd:
+.\.venv\Scripts\activate.bat
+pip install -r requirements.txt

requirements.txt

@@ -1,9 +1,9 @@
 fastapi==0.95.2
 uvicorn[standard]==0.22.0
-torch==2.1.0+cpu
-torchaudio==2.1.0+cpu
+torch==2.1.0
+torchaudio==2.1.0
 numpy==1.26.4
 pillow==10.1.0
 requests==2.32.2
-boto3==1.30.24
-botocore==1.35.24
+huggingface-hub>=0.14.1
+gradio==3.39.0