added all files

README.md

@@ -1,12 +1,13 @@
 ---
-title: Fspen
-emoji: 🔥
-colorFrom: yellow
-colorTo: pink
+title: DeepFilterNet2
+emoji: 💩
+colorFrom: gray
+colorTo: red
 sdk: gradio
-sdk_version: 6.2.0
 app_file: app.py
+sdk_version: 3.17.1
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -0,0 +1,285 @@
+import glob
+import math
+import os
+import sys
+import tempfile
+import time
+from typing import List, Optional, Tuple, Union
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torchaudio
+import torchaudio.transforms as T
+from loguru import logger
+from PIL import Image
+
+sys.path.append("fspen")
+from fspen.src.test import enhance_audio
+
+CHECKPOINT_PATH = ""
+TARGET_SR = 16000
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# --- PLOTTING SETUP ---
+fig_noisy: plt.Figure
+fig_enh: plt.Figure
+ax_noisy: plt.Axes
+ax_enh: plt.Axes
+fig_noisy, ax_noisy = plt.subplots(figsize=(15.2, 4))
+fig_noisy.set_tight_layout(True)
+fig_enh, ax_enh = plt.subplots(figsize=(15.2, 4))
+fig_enh.set_tight_layout(True)
+
+NOISES = {
+    "None": None,
+    "Kitchen": "samples/dkitchen.wav",
+    "Living Room": "samples/dliving.wav",
+    "River": "samples/nriver.wav",
+    "Cafe": "samples/scafe.wav",
+}
+
+# --- HELPER FUNCTIONS ---
+
+
+def load_audio_torch(path, target_sr=TARGET_SR):
+    """Replacement for df.load_audio using torchaudio"""
+    if path is None:
+        return None, None
+
+    sig, sr = torchaudio.load(path)
+    if sr != target_sr:
+        resampler = T.Resample(sr, target_sr)
+        sig = resampler(sig)
+    return sig, target_sr
+
+
+def save_audio_torch(path, tensor, sr):
+    """Replacement for df.save_audio using torchaudio"""
+    # Ensure tensor is on CPU
+    tensor = tensor.detach().cpu()
+    # Check shape [channels, time], torchaudio expects this
+    if tensor.dim() == 1:
+        tensor = tensor.unsqueeze(0)
+    torchaudio.save(path, tensor, sr)
+
+
+def mix_at_snr(clean, noise, snr, eps=1e-10):
+    """Mix clean and noise signal at a given SNR."""
+    # Standardize to (1, T)
+    if clean.dim() == 1:
+        clean = clean.unsqueeze(0)
+    if noise.dim() == 1:
+        noise = noise.unsqueeze(0)
+
+    clean = clean.mean(0, keepdim=True)
+    noise = noise.mean(0, keepdim=True)
+
+    if noise.shape[1] < clean.shape[1]:
+        noise = noise.repeat((1, int(math.ceil(clean.shape[1] / noise.shape[1]))))
+    max_start = int(noise.shape[1] - clean.shape[1])
+    start = torch.randint(0, max_start, ()).item() if max_start > 0 else 0
+    noise = noise[:, start : start + clean.shape[1]]
+
+    E_speech = torch.mean(clean.pow(2)) + eps
+    E_noise = torch.mean(noise.pow(2))
+    K = torch.sqrt((E_noise / E_speech) * 10 ** (snr / 10) + eps)
+    noise = noise / K
+    mixture = clean + noise
+
+    max_m = mixture.abs().max()
+    if max_m > 1:
+        clean, noise, mixture = clean / max_m, noise / max_m, mixture / max_m
+    return clean, noise, mixture
+
+
+def specshow(
+    spec,
+    ax=None,
+    title=None,
+    sr=48000,
+    n_fft=None,
+    hop=None,
+    t=None,
+    f=None,
+    vmin=-100,
+    vmax=0,
+    cmap="inferno",
+):
+    """Plots a spectrogram of shape [F, T]"""
+    spec_np = spec.cpu().numpy() if isinstance(spec, torch.Tensor) else spec
+    if ax is None:
+        ax = plt
+
+    if n_fft is None:
+        n_fft = (spec.shape[0] - 1) * 2
+    hop = hop or n_fft // 4
+
+    if t is None:
+        t = np.arange(0, spec_np.shape[-1]) * hop / sr
+    if f is None:
+        f = np.arange(0, spec_np.shape[0]) * sr // 2 / (n_fft // 2) / 1000
+
+    im = ax.pcolormesh(
+        t, f, spec_np, rasterized=True, shading="auto", vmin=vmin, vmax=vmax, cmap=cmap
+    )
+    if title:
+        ax.set_title(title)
+    ax.set_xlabel("Time [s]")
+    ax.set_ylabel("Frequency [kHz]")
+    return im
+
+
+def spec_im(audio: torch.Tensor, sr=TARGET_SR, figsize=(15, 5), figure=None, ax=None) -> Image:
+    audio = torch.as_tensor(audio)
+    if audio.dim() > 1:
+        audio = audio.mean(dim=0)  # Mix to mono for spec
+
+    n_fft = 1024
+    hop = 512
+    w = torch.hann_window(n_fft, device=audio.device)
+    spec = torch.stft(audio, n_fft, hop, window=w, return_complex=False)
+    spec = spec.div_(w.pow(2).sum())
+    spec = torch.view_as_complex(spec).abs().clamp_min(1e-12).log10().mul(10)
+
+    if figure is None:
+        figure = plt.figure(figsize=figsize)
+        figure.set_tight_layout(True)
+
+    if spec.dim() > 2:
+        spec = spec.squeeze(0)
+    specshow(spec, ax=ax, sr=sr, n_fft=n_fft, hop=hop)
+
+    figure.canvas.draw()
+    return Image.frombytes("RGB", figure.canvas.get_width_height(), figure.canvas.tostring_rgb())
+
+
+def cleanup_tmp(filter_list: List[str] = [], hours_keep=2):
+    # Basic cleanup logic
+    if os.path.exists("/tmp"):
+        for f in glob.glob("/tmp/*wav"):
+            # Only delete if very old or explicitly temp
+            pass
+
+
+# --- MAIN DEMO FUNCTION ---
+
+
+def demo_fn(speech_upl: str, noise_type: str, snr: int, mic_input: Optional[str] = None):
+    if mic_input:
+        speech_upl = mic_input
+
+    sr = TARGET_SR
+    logger.info(f"Params: speech={speech_upl}, noise={noise_type}, snr={snr}")
+    snr = int(snr)
+    noise_fn = NOISES[noise_type]
+
+    # 1. Load Clean Speech
+    max_s = 10
+    if speech_upl is not None:
+        sample, _ = load_audio_torch(speech_upl, sr)
+        max_len = max_s * sr
+        if sample.shape[-1] > max_len:
+            start = torch.randint(0, sample.shape[-1] - max_len, ()).item()
+            sample = sample[..., start : start + max_len]
+    else:
+        # Fallback sample
+        sample, _ = load_audio_torch("samples/p232_013_clean.wav", sr)
+        sample = sample[..., : max_s * sr]
+
+    # Ensure channels first
+    if sample.dim() > 1 and sample.shape[0] > 1:
+        sample = sample.mean(dim=0, keepdim=True)
+
+    # 2. Add Noise (if selected)
+    if noise_fn is not None:
+        noise, _ = load_audio_torch(noise_fn, sr)
+        _, _, sample = mix_at_snr(sample, noise, snr)
+
+    # 3. Save Noisy File (Input for enhance_audio)
+    noisy_wav_path = tempfile.NamedTemporaryFile(suffix="noisy.wav", delete=False).name
+    save_audio_torch(noisy_wav_path, sample, sr)
+
+    # 4. Run Inference using your Custom Function
+    enhanced_wav_path = tempfile.NamedTemporaryFile(suffix="enhanced.wav", delete=False).name
+
+    logger.info("Starting enhancement...")
+    # CALLING YOUR MODEL HERE
+    enhance_audio(CHECKPOINT_PATH, noisy_wav_path, enhanced_wav_path)
+    logger.info("Enhancement finished")
+
+    # 5. Load Enhanced Audio for Visualization
+    enhanced, _ = load_audio_torch(enhanced_wav_path, sr)
+
+    # 6. Generate Visuals
+    ax_noisy.clear()
+    ax_enh.clear()
+    noisy_im = spec_im(sample, sr=sr, figure=fig_noisy, ax=ax_noisy)
+    enh_im = spec_im(enhanced, sr=sr, figure=fig_enh, ax=ax_enh)
+
+    return noisy_wav_path, noisy_im, enhanced_wav_path, enh_im
+
+
+def toggle(choice):
+    if choice == "mic":
+        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
+    else:
+        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
+
+
+# --- GRADIO INTERFACE ---
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown(
+            """
+            ## Audio Enhancement Demo (Custom Model)
+            Upload audio or record from mic to test the model in `mva-proj`.
+            """
+        )
+    with gr.Row():
+        with gr.Column():
+            radio = gr.Radio(["mic", "file"], value="file", label="Audio Source")
+            mic_input = gr.Mic(label="Microphone Input", type="filepath", visible=False)
+            audio_file = gr.Audio(type="filepath", label="File Input", visible=True)
+            inputs = [
+                audio_file,
+                gr.Dropdown(
+                    label="Add background noise",
+                    choices=list(NOISES.keys()),
+                    value="None",
+                ),
+                gr.Dropdown(
+                    label="Noise Level (SNR)",
+                    choices=["-5", "0", "10", "20"],
+                    value="10",
+                ),
+                mic_input,
+            ]
+            btn = gr.Button("Denoise", variant="primary")
+        with gr.Column():
+            outputs = [
+                gr.Audio(type="filepath", label="Noisy Input"),
+                gr.Image(label="Noisy Spectrogram"),
+                gr.Audio(type="filepath", label="Enhanced Output"),
+                gr.Image(label="Enhanced Spectrogram"),
+            ]
+
+    btn.click(fn=demo_fn, inputs=inputs, outputs=outputs, api_name="denoise")
+    radio.change(toggle, radio, [mic_input, audio_file])
+
+    # Examples (Ensure these files exist in your folder)
+    if os.path.exists("samples/p232_013_clean.wav"):
+        gr.Examples(
+            [
+                ["samples/p232_013_clean.wav", "Kitchen", "10"],
+                ["samples/p232_013_clean.wav", "Cafe", "10"],
+            ],
+            fn=demo_fn,
+            inputs=inputs,
+            outputs=outputs,
+            cache_examples=False,  # Disable cache if model changes frequently
+        )
+
+demo.launch(enable_queue=True)

fspen/.gitignore

@@ -0,0 +1,158 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+./data/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+### VisualStudioCode
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+*.code-workspace
+**/.vscode
+
+# JetBrains
+.idea/
+
+# Data & Models
+*.h5
+*.tar
+*.tar.gz
+
+# Lightning-Hydra-Template
+configs/local/default.yaml
+/data/
+/logs/
+.env
+
+# Aim logging
+.aim
+Fspen_an_Ultra-Lightweight_Network_for_Real_Time_Speech_Enahncment.pdf
+voicebank_data
+voicebank_wavs

fspen/.pre-commit-config.yaml

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+default_language_version:
+  python: python3
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v6.0.0
+    hooks:
+      # list of supported hooks: https://pre-commit.com/hooks.html
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-docstring-first
+      - id: check-yaml
+      - id: debug-statements
+      - id: detect-private-key
+      - id: check-executables-have-shebangs
+      - id: check-toml
+      - id: check-case-conflict
+      - id: check-added-large-files
+
+  # python code formatting
+  - repo: https://github.com/psf/black
+    rev: 25.12.0
+    hooks:
+      - id: black
+        args: [--line-length, "99"]
+
+  # python import sorting
+  - repo: https://github.com/PyCQA/isort
+    rev: 7.0.0
+    hooks:
+      - id: isort
+        args: ["--profile", "black", "--filter-files"]
+
+  # python upgrading syntax to newer version
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.21.2
+    hooks:
+      - id: pyupgrade
+        args: [--py38-plus]
+
+  # python docstring formatting
+  - repo: https://github.com/myint/docformatter
+    rev: v1.7.7
+    hooks:
+      - id: docformatter
+        args:
+          [
+            --in-place,
+            --wrap-summaries=99,
+            --wrap-descriptions=99,
+            --style=sphinx,
+            --black,
+          ]
+
+  # # python docstring coverage checking
+  # - repo: https://github.com/econchick/interrogate
+  #   rev: 1.7.0 # or master if you're bold
+  #   hooks:
+  #     - id: interrogate
+  #       args:
+  #         [
+  #           --verbose,
+  #           --fail-under=80,
+  #           --ignore-init-module,
+  #           --ignore-init-method,
+  #           --ignore-module,
+  #           --ignore-nested-functions,
+  #           -vv,
+  #         ]
+
+  # python check (PEP8), programming errors and code complexity
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.3.0
+    hooks:
+      - id: flake8
+        args:
+          [
+            "--extend-ignore",
+            "E203,E402,E501,F401,F841,RST2,RST301",
+            "--exclude",
+            "logs/*,data/*",
+          ]
+        additional_dependencies: [flake8-rst-docstrings==0.3.0]
+
+  # python security linter
+  - repo: https://github.com/PyCQA/bandit
+    rev: "1.9.2"
+    hooks:
+      - id: bandit
+        args: ["-s", "B101,B311"]
+
+  # yaml formatting
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v4.0.0-alpha.8
+    hooks:
+      - id: prettier
+        types: [yaml]
+        exclude: "environment.yaml"
+
+  # shell scripts linter
+  - repo: https://github.com/shellcheck-py/shellcheck-py
+    rev: v0.11.0.1
+    hooks:
+      - id: shellcheck
+
+  # word spelling linter
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.4.1
+    hooks:
+      - id: codespell
+        args:
+          - --skip=logs/**,data/**,*.ipynb
+          # - --ignore-words-list=abc,def
+
+  # jupyter notebook cell output clearing
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.8.2
+    hooks:
+      - id: nbstripout
+
+  # jupyter notebook linting
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.9.1
+    hooks:
+      - id: nbqa-black
+        args: ["--line-length=99"]
+      - id: nbqa-isort
+        args: ["--profile=black"]
+      - id: nbqa-flake8
+        args:
+          [
+            "--extend-ignore=E203,E402,E501,F401,F841",
+            "--exclude=logs/*,data/*",
+          ]

fspen/.project-root

@@ -0,0 +1,2 @@
+# this file is required for inferring the project root directory
+# do not delete

fspen/Makefile

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+
+help:  ## Show help
+	@grep -E '^[.a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}'
+
+clean: ## Clean autogenerated files
+	rm -rf dist
+	find . -type f -name "*.DS_Store" -ls -delete
+	find . | grep -E "(__pycache__|\.pyc|\.pyo)" | xargs rm -rf
+	find . | grep -E ".pytest_cache" | xargs rm -rf
+	find . | grep -E ".ipynb_checkpoints" | xargs rm -rf
+	rm -f .coverage
+
+clean-logs: ## Clean logs
+	rm -rf logs/**
+
+format: ## Run pre-commit hooks
+	pre-commit run -a
+
+sync: ## Merge changes from main branch to your current branch
+	git pull
+	git pull origin main
+
+test: ## Run not slow tests
+	pytest -k "not slow"
+
+test-full: ## Run all tests
+	pytest
+
+train: ## Train the model
+	python src/train.py

fspen/README.md

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+<div align="center">
+
+# Your Project Name
+
+<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
+<a href="https://pytorchlightning.ai/"><img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a>
+<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a>
+<a href="https://github.com/ashleve/lightning-hydra-template"><img alt="Template" src="https://img.shields.io/badge/-Lightning--Hydra--Template-017F2F?style=flat&logo=github&labelColor=gray"></a><br>
+[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10446016)
+
+</div>
+
+## Description
+
+What it does
+
+## Installation
+
+#### Pip
+
+```bash
+# clone project
+git clone https://github.com/iliasslasri/
+cd
+
+# [OPTIONAL] create conda environment
+conda create -n myenv python=3.9
+conda activate myenv
+
+# install pytorch according to instructions
+# https://pytorch.org/get-started/
+
+# install requirements
+pip install -r requirements.txt
+```
+
+#### Conda
+
+```bash
+# clone project
+git clone https://github.com/YourGithubName/your-repo-name
+cd your-repo-name
+
+# create conda environment and install dependencies
+conda env create -f environment.yaml -n myenv
+
+# activate conda environment
+conda activate myenv
+```
+
+## How to run
+
+Train model with default configuration
+
+```bash
+# train on CPU
+python src/train.py trainer=cpu
+
+# train on GPU
+python src/train.py trainer=gpu
+```
+
+Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/)
+
+```bash
+python src/train.py experiment=experiment_name.yaml
+```
+
+You can override any parameter from command line like this
+
+```bash
+python src/train.py trainer.max_epochs=20 data.batch_size=64
+```
+
+
+
+
+## Set up environment for dev
+```bash
+pre-commit install
+
+pip install \
+  "torch==2.0.1+cu118" \
+  "torchvision==0.15.2+cu118" \
+  "torchaudio==2.0.2+cu118" \
+  "lightning==2.0.9" \
+  "torchmetrics==0.11.4" \
+  "numpy<2.0" \
+  "pesq" \
+  "hydra-colorlog" \
+  --extra-index-url https://download.pytorch.org/whl/cu118
+
+python3 src/train.py callbacks.rich_progress_bar=null
+```

fspen/configs/__init__.py

@@ -0,0 +1 @@
+# this file is needed here to include configs when building project as a package

fspen/configs/callbacks/default.yaml

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+defaults:
+  - model_checkpoint
+  - early_stopping
+  - model_summary
+  - rich_progress_bar
+  - _self_
+
+model_checkpoint:
+  dirpath: ${paths.output_dir}/checkpoints
+  filename: "epoch_{epoch:03d}"
+  monitor: "val/loss"
+  mode: "min"
+  save_last: True
+  auto_insert_metric_name: False
+  save_top_k: 3
+
+early_stopping:
+  monitor: "val/loss"
+  patience: 100
+  mode: "max"
+
+model_summary:
+  max_depth: -1

fspen/configs/callbacks/early_stopping.yaml

@@ -0,0 +1,15 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html
+
+early_stopping:
+  _target_: lightning.pytorch.callbacks.EarlyStopping
+  monitor: ??? # quantity to be monitored, must be specified !!!
+  min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
+  patience: 3 # number of checks with no improvement after which training will be stopped
+  verbose: False # verbosity mode
+  mode: "min" # "max" means higher metric value is better, can be also "min"
+  strict: True # whether to crash the training if monitor is not found in the validation metrics
+  check_finite: True # when set True, stops training when the monitor becomes NaN or infinite
+  stopping_threshold: null # stop training immediately once the monitored quantity reaches this threshold
+  divergence_threshold: null # stop training as soon as the monitored quantity becomes worse than this threshold
+  check_on_train_epoch_end: null # whether to run early stopping at the end of the training epoch
+  # log_rank_zero_only: False  # this keyword argument isn't available in stable version

fspen/configs/callbacks/model_checkpoint.yaml

@@ -0,0 +1,17 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
+
+model_checkpoint:
+  _target_: lightning.pytorch.callbacks.ModelCheckpoint
+  dirpath: null # directory to save the model file
+  filename: null # checkpoint filename
+  monitor: null # name of the logged metric which determines when model is improving
+  verbose: False # verbosity mode
+  save_last: null # additionally always save an exact copy of the last checkpoint to a file last.ckpt
+  save_top_k: 1 # save k best models (determined by above metric)
+  mode: "min" # "max" means higher metric value is better, can be also "min"
+  auto_insert_metric_name: True # when True, the checkpoints filenames will contain the metric name
+  save_weights_only: False # if True, then only the model’s weights will be saved
+  every_n_train_steps: null # number of training steps between checkpoints
+  train_time_interval: null # checkpoints are monitored at the specified time interval
+  every_n_epochs: null # number of epochs between checkpoints
+  save_on_train_epoch_end: null # whether to run checkpointing at the end of the training epoch or the end of validation

fspen/configs/callbacks/model_summary.yaml

@@ -0,0 +1,5 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.RichModelSummary.html
+
+model_summary:
+  _target_: lightning.pytorch.callbacks.RichModelSummary
+  max_depth: 1 # the maximum depth of layer nesting that the summary will include

fspen/configs/callbacks/rich_progress_bar.yaml

@@ -0,0 +1,4 @@
+# https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.RichProgressBar.html
+
+rich_progress_bar:
+  _target_: lightning.pytorch.callbacks.RichProgressBar

fspen/configs/data/speech_enhancement.yaml

@@ -0,0 +1,13 @@
+_target_: src.data.datamodule.SpeechEnhancementDataModule
+dataset:
+  _target_: src.data.dataset.SpeechEnhancementDataset
+  sample_rate: 16000
+  segment_len: 10.0 # in seconds
+  n_fft: 512
+  hop_length: 128
+  win_length: 512
+  noisy_dir: ${paths.noisy_dir}
+  clean_dir: ${paths.clean_dir}
+batch_size: 64 # Needs to be divisible by the number of devices (e.g., if in a distributed setup)
+val_split: 0.1
+num_workers: 2

fspen/configs/debug/default.yaml

@@ -0,0 +1,35 @@
+# @package _global_
+
+# default debugging setup, runs 1 full epoch
+# other debugging configs can inherit from this one
+
+# overwrite task name so debugging logs are stored in separate folder
+task_name: "debug"
+
+# disable callbacks and loggers during debugging
+callbacks: null
+logger: null
+
+extras:
+  ignore_warnings: False
+  enforce_tags: False
+
+# sets level of all command line loggers to 'DEBUG'
+# https://hydra.cc/docs/tutorials/basic/running_your_app/logging/
+hydra:
+  job_logging:
+    root:
+      level: DEBUG
+
+  # use this to also set hydra loggers to 'DEBUG'
+  # verbose: True
+
+trainer:
+  max_epochs: 1
+  accelerator: cpu # debuggers don't like gpus
+  devices: 1 # debuggers don't like multiprocessing
+  detect_anomaly: true # raise exception if NaN or +/-inf is detected in any tensor
+
+data:
+  num_workers: 0 # debuggers don't like multiprocessing
+  pin_memory: False # disable gpu memory pin

fspen/configs/debug/fdr.yaml

@@ -0,0 +1,9 @@
+# @package _global_
+
+# runs 1 train, 1 validation and 1 test step
+
+defaults:
+  - default
+
+trainer:
+  fast_dev_run: true

fspen/configs/debug/limit.yaml

@@ -0,0 +1,12 @@
+# @package _global_
+
+# uses only 1% of the training data and 5% of validation/test data
+
+defaults:
+  - default
+
+trainer:
+  max_epochs: 3
+  limit_train_batches: 0.01
+  limit_val_batches: 0.05
+  limit_test_batches: 0.05

fspen/configs/debug/overfit.yaml

@@ -0,0 +1,13 @@
+# @package _global_
+
+# overfits to 3 batches
+
+defaults:
+  - default
+
+trainer:
+  max_epochs: 20
+  overfit_batches: 3
+
+# model ckpt and early stopping need to be disabled during overfitting
+callbacks: null

fspen/configs/debug/profiler.yaml

@@ -0,0 +1,12 @@
+# @package _global_
+
+# runs with execution time profiling
+
+defaults:
+  - default
+
+trainer:
+  max_epochs: 1
+  profiler: "simple"
+  # profiler: "advanced"
+  # profiler: "pytorch"

fspen/configs/eval.yaml

@@ -0,0 +1,19 @@
+# @package _global_
+
+defaults:
+  - _self_
+  - data: speech_enhancement
+  - model: fspen
+  - callbacks: default
+  - logger: tensorboard
+  - trainer: gpu
+  - paths: eval
+  - extras: default
+  - hydra: default
+
+task_name: "eval"
+
+tags: ["dev"]
+
+# passing checkpoint path is necessary for evaluation
+ckpt_path: ???

fspen/configs/experiment/example.yaml

@@ -0,0 +1,41 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: mnist
+  - override /model: mnist
+  - override /callbacks: default
+  - override /trainer: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["mnist", "simple_dense_net"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 10
+  max_epochs: 10
+  gradient_clip_val: 0.5
+
+model:
+  optimizer:
+    lr: 0.002
+  net:
+    lin1_size: 128
+    lin2_size: 256
+    lin3_size: 64
+  compile: false
+
+data:
+  batch_size: 64
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "mnist"
+  aim:
+    experiment: "mnist"

fspen/configs/extras/default.yaml

@@ -0,0 +1,8 @@
+# disable python warnings if they annoy you
+ignore_warnings: False
+
+# ask user for tags if none are provided in the config
+enforce_tags: True
+
+# pretty print config tree at the start of the run using Rich library
+print_config: True

fspen/configs/hparams_search/mnist_optuna.yaml

@@ -0,0 +1,52 @@
+# @package _global_
+
+# example hyperparameter optimization of some experiment with Optuna:
+# python train.py -m hparams_search=mnist_optuna experiment=example
+
+defaults:
+  - override /hydra/sweeper: optuna
+
+# choose metric which will be optimized by Optuna
+# make sure this is the correct name of some metric logged in lightning module!
+optimized_metric: "val/acc_best"
+
+# here we define Optuna hyperparameter search
+# it optimizes for value returned from function with @hydra.main decorator
+# docs: https://hydra.cc/docs/next/plugins/optuna_sweeper
+hydra:
+  mode: "MULTIRUN" # set hydra to multirun by default if this config is attached
+
+  sweeper:
+    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper
+
+    # storage URL to persist optimization results
+    # for example, you can use SQLite if you set 'sqlite:///example.db'
+    storage: null
+
+    # name of the study to persist optimization results
+    study_name: null
+
+    # number of parallel workers
+    n_jobs: 1
+
+    # 'minimize' or 'maximize' the objective
+    direction: maximize
+
+    # total number of runs that will be executed
+    n_trials: 20
+
+    # choose Optuna hyperparameter sampler
+    # you can choose bayesian sampler (tpe), random search (without optimization), grid sampler, and others
+    # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html
+    sampler:
+      _target_: optuna.samplers.TPESampler
+      seed: 1234
+      n_startup_trials: 10 # number of random sampling runs before optimization starts
+
+    # define hyperparameter search space
+    params:
+      model.optimizer.lr: interval(0.0001, 0.1)
+      data.batch_size: choice(32, 64, 128, 256)
+      model.net.lin1_size: choice(64, 128, 256)
+      model.net.lin2_size: choice(64, 128, 256)
+      model.net.lin3_size: choice(32, 64, 128, 256)

fspen/configs/hydra/default.yaml

@@ -0,0 +1,19 @@
+# https://hydra.cc/docs/configure_hydra/intro/
+
+# enable color logging
+defaults:
+  - override hydra_logging: colorlog
+  - override job_logging: colorlog
+
+# output directory, generated dynamically on each run
+run:
+  dir: ${paths.log_dir}/${task_name}/runs/${now:%Y-%m-%d}_${now:%H-%M-%S}
+sweep:
+  dir: ${paths.log_dir}/${task_name}/multiruns/${now:%Y-%m-%d}_${now:%H-%M-%S}
+  subdir: ${hydra.job.num}
+
+job_logging:
+  handlers:
+    file:
+      # Incorporates fix from https://github.com/facebookresearch/hydra/pull/2242
+      filename: ${hydra.runtime.output_dir}/${task_name}.log

fspen/configs/logger/aim.yaml

@@ -0,0 +1,28 @@
+# https://aimstack.io/
+
+# example usage in lightning module:
+# https://github.com/aimhubio/aim/blob/main/examples/pytorch_lightning_track.py
+
+# open the Aim UI with the following command (run in the folder containing the `.aim` folder):
+# `aim up`
+
+aim:
+  _target_: aim.pytorch_lightning.AimLogger
+  repo: ${paths.root_dir} # .aim folder will be created here
+  # repo: "aim://ip_address:port" # can instead provide IP address pointing to Aim remote tracking server which manages the repo, see https://aimstack.readthedocs.io/en/latest/using/remote_tracking.html#
+
+  # aim allows to group runs under experiment name
+  experiment: null # any string, set to "default" if not specified
+
+  train_metric_prefix: "train/"
+  val_metric_prefix: "val/"
+  test_metric_prefix: "test/"
+
+  # sets the tracking interval in seconds for system usage metrics (CPU, GPU, memory, etc.)
+  system_tracking_interval: 10 # set to null to disable system metrics tracking
+
+  # enable/disable logging of system params such as installed packages, git info, env vars, etc.
+  log_system_params: true
+
+  # enable/disable tracking console logs (default value is true)
+  capture_terminal_logs: false # set to false to avoid infinite console log loop issue https://github.com/aimhubio/aim/issues/2550

fspen/configs/logger/comet.yaml

@@ -0,0 +1,12 @@
+# https://www.comet.ml
+
+comet:
+  _target_: lightning.pytorch.loggers.comet.CometLogger
+  api_key: ${oc.env:COMET_API_TOKEN} # api key is loaded from environment variable
+  save_dir: "${paths.output_dir}"
+  project_name: "lightning-hydra-template"
+  rest_api_key: null
+  # experiment_name: ""
+  experiment_key: null # set to resume experiment
+  offline: False
+  prefix: ""

fspen/configs/logger/csv.yaml

@@ -0,0 +1,7 @@
+# csv logger built in lightning
+
+csv:
+  _target_: lightning.pytorch.loggers.csv_logs.CSVLogger
+  save_dir: "${paths.output_dir}"
+  name: "csv/"
+  prefix: ""

fspen/configs/logger/many_loggers.yaml

@@ -0,0 +1,9 @@
+# train with many loggers at once
+
+defaults:
+  # - comet
+  - csv
+  # - mlflow
+  # - neptune
+  - tensorboard
+  - wandb

fspen/configs/logger/mlflow.yaml

@@ -0,0 +1,12 @@
+# https://mlflow.org
+
+mlflow:
+  _target_: lightning.pytorch.loggers.mlflow.MLFlowLogger
+  # experiment_name: ""
+  # run_name: ""
+  tracking_uri: ${paths.log_dir}/mlflow/mlruns # run `mlflow ui` command inside the `logs/mlflow/` dir to open the UI
+  tags: null
+  # save_dir: "./mlruns"
+  prefix: ""
+  artifact_location: null
+  # run_id: ""

fspen/configs/logger/neptune.yaml

@@ -0,0 +1,9 @@
+# https://neptune.ai
+
+neptune:
+  _target_: lightning.pytorch.loggers.neptune.NeptuneLogger
+  api_key: ${oc.env:NEPTUNE_API_TOKEN} # api key is loaded from environment variable
+  project: username/lightning-hydra-template
+  # name: ""
+  log_model_checkpoints: True
+  prefix: ""

fspen/configs/logger/tensorboard.yaml

@@ -0,0 +1,10 @@
+# https://www.tensorflow.org/tensorboard/
+
+tensorboard:
+  _target_: lightning.pytorch.loggers.tensorboard.TensorBoardLogger
+  save_dir: "${paths.output_dir}/tensorboard/"
+  name: null
+  log_graph: False
+  default_hp_metric: True
+  prefix: ""
+  # version: ""

fspen/configs/logger/wandb.yaml

@@ -0,0 +1,16 @@
+# https://wandb.ai
+
+wandb:
+  _target_: lightning.pytorch.loggers.wandb.WandbLogger
+  # name: "" # name of the run (normally generated by wandb)
+  save_dir: "${paths.output_dir}"
+  offline: False
+  id: null # pass correct id to resume experiment!
+  anonymous: null # enable anonymous logging
+  project: "lightning-hydra-template"
+  log_model: False # upload lightning ckpts
+  prefix: "" # a string to put at the beginning of metric keys
+  # entity: "" # set to name of your wandb team
+  group: ""
+  tags: []
+  job_type: ""

fspen/configs/model/fspen.yaml

@@ -0,0 +1,24 @@
+_target_: src.models.fspen_module.FSPENLitModule
+
+optimizer:
+  _target_: torch.optim.Adam
+  _partial_: true
+  lr: 0.001
+  weight_decay: 0.0
+
+scheduler:
+  _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+  _partial_: true
+  mode: min
+  factor: 0.1
+  patience: 10
+
+net:
+  _target_: src.models.fspen.FSPEN
+
+# compile model for faster training with pytorch 2.0
+compile: false
+
+criterion:
+  _target_: src.models.components.loss.MultiResolutionSTFTLoss
+  fft_sizes: [512, 1024, 2048]

fspen/configs/paths/default.yaml

@@ -0,0 +1,19 @@
+# path to root directory
+# this requires PROJECT_ROOT environment variable to exist
+# you can replace it with "." if you want the root to be the current working directory
+root_dir: ${oc.env:PROJECT_ROOT}
+
+# path to data directory
+noisy_dir: ${paths.root_dir}/data/noisy/
+clean_dir: ${paths.root_dir}/data/clean/
+
+# path to logging directory
+log_dir: ${paths.root_dir}/logs/
+
+# path to output directory, created dynamically by hydra
+# path generation pattern is specified in `configs/hydra/default.yaml`
+# use it to store all files generated during the run, like ckpts and metrics
+output_dir: ${hydra:runtime.output_dir}
+
+# path to working directory
+work_dir: ${hydra:runtime.cwd}

fspen/configs/paths/eval.yaml

@@ -0,0 +1,19 @@
+# path to root directory
+# this requires PROJECT_ROOT environment variable to exist
+# you can replace it with "." if you want the root to be the current working directory
+root_dir: ${oc.env:PROJECT_ROOT}
+
+# path to data directory
+noisy_dir: ${paths.root_dir}/voicebank_wavs/test/noisy/
+clean_dir: ${paths.root_dir}/voicebank_wavs/test/clean/
+
+# path to logging directory
+log_dir: ${paths.root_dir}/logs/
+
+# path to output directory, created dynamically by hydra
+# path generation pattern is specified in `configs/hydra/default.yaml`
+# use it to store all files generated during the run, like ckpts and metrics
+output_dir: ${hydra:runtime.output_dir}
+
+# path to working directory
+work_dir: ${hydra:runtime.cwd}

fspen/configs/train.yaml

@@ -0,0 +1,49 @@
+# @package _global_
+
+# specify here default configuration
+# order of defaults determines the order in which configs override each other
+defaults:
+  - _self_
+  - data: speech_enhancement
+  - model: fspen
+  - callbacks: default
+  - logger: tensorboard
+  - trainer: gpu
+  - paths: default
+  - extras: default
+  - hydra: default
+
+  # experiment configs allow for version control of specific hyperparameters
+  # e.g. best hyperparameters for given model and datamodule
+  - experiment: null
+
+  # config for hyperparameter optimization
+  - hparams_search: null
+
+  # optional local config for machine/user specific settings
+  # it's optional since it doesn't need to exist and is excluded from version control
+  - optional local: default
+
+  # debugging config (enable through command line, e.g. `python train.py debug=default)
+  - debug: null
+
+# task name, determines output directory path
+task_name: "train"
+
+# tags to help you identify your experiments
+# you can overwrite this in experiment configs
+# overwrite from command line with `python train.py tags="[first_tag, second_tag]"`
+tags: ["dev"]
+
+# set False to skip model training
+train: True
+
+# evaluate on test set, using best model weights achieved during training
+# lightning chooses best weights based on the metric specified in checkpoint callback
+test: False
+
+# simply provide checkpoint path to resume training
+ckpt_path: null
+
+# seed for random number generators in pytorch, numpy and python.random
+seed: null

fspen/configs/trainer/cpu.yaml

@@ -0,0 +1,5 @@
+defaults:
+  - default
+
+accelerator: cpu
+devices: 1

fspen/configs/trainer/ddp.yaml

@@ -0,0 +1,9 @@
+defaults:
+  - default
+
+strategy: ddp
+
+accelerator: gpu
+devices: 4
+num_nodes: 1
+sync_batchnorm: True

fspen/configs/trainer/ddp_sim.yaml

@@ -0,0 +1,7 @@
+defaults:
+  - default
+
+# simulate DDP on CPU, useful for debugging
+accelerator: cpu
+devices: 2
+strategy: ddp_spawn

fspen/configs/trainer/default.yaml

@@ -0,0 +1,19 @@
+_target_: lightning.pytorch.trainer.Trainer
+
+default_root_dir: ${paths.output_dir}
+
+min_epochs: 1 # prevents early stopping
+max_epochs: 100
+
+accelerator: cpu
+devices: 1
+
+# mixed precision for extra speed-up
+# precision: 16
+
+# perform a validation loop every N training epochs
+check_val_every_n_epoch: 1
+
+# set True to to ensure deterministic results
+# makes training slower but gives more reproducibility than just setting seeds
+deterministic: False

fspen/configs/trainer/gpu.yaml

@@ -0,0 +1,5 @@
+defaults:
+  - default
+
+accelerator: gpu
+devices: 1

fspen/configs/trainer/mps.yaml

@@ -0,0 +1,5 @@
+defaults:
+  - default
+
+accelerator: mps
+devices: 1

fspen/environment.yaml

@@ -0,0 +1,125 @@
+##############
+## This environment uses old versions of dl libraries because the training are done on an old GPU
+## mainly torch==2.0.1+cu118
+##############
+name: fspen
+channels:
+  - defaults
+  - https://repo.anaconda.com/pkgs/main
+  - https://repo.anaconda.com/pkgs/r
+dependencies:
+  - _libgcc_mutex=0.1
+  - _openmp_mutex=5.1
+  - bzip2=1.0.8
+  - ca-certificates=2025.9.9
+  - expat=2.7.1
+  - ld_impl_linux-64=2.44
+  - libffi=3.4.4
+  - libgcc-ng=11.2.0
+  - libgomp=11.2.0
+  - libnsl=2.0.0
+  - libstdcxx-ng=11.2.0
+  - libuuid=1.41.5
+  - libxcb=1.17.0
+  - libzlib=1.3.1
+  - ncurses=6.5
+  - openssl=3.0.18
+  - pip=25.2
+  - pthread-stubs=0.3
+  - python=3.10.19
+  - readline=8.3
+  - setuptools=80.9.0
+  - sqlite=3.50.2
+  - tk=8.6.15
+  - wheel=0.45.1
+  - xorg-libx11=1.8.12
+  - xorg-libxau=1.0.12
+  - xorg-libxdmcp=1.1.5
+  - xorg-xorgproto=2024.1
+  - xz=5.6.4
+  - zlib=1.3.1
+  - pip:
+      - absl-py==2.3.1
+      - aiohappyeyeballs==2.6.1
+      - aiohttp==3.13.2
+      - aiosignal==1.4.0
+      - antlr4-python3-runtime==4.9.3
+      - async-timeout==5.0.1
+      - certifi==2025.10.5
+      - charset-normalizer==3.4.4
+      - cmake==3.25.0
+      - contourpy==1.3.2
+      - cycler==0.12.1
+      - decorator==5.2.1
+      - deprecate==1.0.5
+      - dotenv==0.9.9
+      - einops==0.8.1
+      - filelock==3.19.1
+      - fonttools==4.60.1
+      - frozenlist==1.8.0
+      - fsspec==2025.9.0
+      - grpcio==1.76.0
+      - h5py==3.15.1
+      - hf-xet==1.1.10
+      - huggingface-hub==0.35.3
+      - hydra-colorlog==1.2.0
+      - hydra-core==1.3.2
+      - hydra-optuna-sweeper==1.2.0
+      - idna==3.11
+      - indic-numtowords==1.1.0
+      - iniconfig==2.3.0
+      - ipdb==0.13.13
+      - jinja2==3.1.6
+      - kiwisolver==1.4.9
+      - lightning==2.3.0
+      - lightning-utilities==0.15.2
+      - lit==15.0.7
+      - markdown==3.9
+      - markupsafe==2.1.5
+      - matplotlib==3.10.7
+      - more-itertools==10.8.0
+      - mpmath==1.3.0
+      - multidict==6.7.0
+      - numpy==1.26.4
+      - omegaconf==2.3.0
+      - pandas==2.3.3
+      - pexpect==4.9.0
+      - pillow==12.0.0
+      - pluggy==1.6.0
+      - pre-commit==4.5.0
+      - propcache==0.4.1
+      - protobuf==6.33.0
+      - ptyprocess==0.7.0
+      - pygments==2.19.2
+      - pyparsing==3.2.5
+      - pytest==9.0.1
+      - python-dotenv==1.2.1
+      - pytz==2025.2
+      - pyyaml==6.0.3
+      - regex==2025.10.23
+      - rich==14.2.0
+      - rootutils==1.0.7
+      - safetensors==0.6.2
+      - scipy==1.15.3
+      - six==1.17.0
+      - soundfile==0.13.1
+      - sympy==1.14.0
+      - tensorboard==2.20.0
+      - tensorboard-data-server==0.7.2
+      - tokenizers==0.22.1
+      - torch==2.0.1+cu118
+      - torch-tb-profiler==0.4.3
+      - torchaudio==2.0.2+cu118
+      - torchinfo==1.8.0
+      - torchmetrics==0.11.4
+      - torchsummary==1.5.1
+      - tqdm==4.67.1
+      - transformers==4.57.1
+      - triton==2.0.0
+      - typing-extensions==4.15.0
+      - tzdata==2025.2
+      - urllib3==2.5.0
+      - werkzeug==3.1.3
+      - xxhash==3.6.0
+      - yarl==1.22.0
+prefix: /home/infres/lasri-22/miniconda3/envs/fspen

fspen/pyproject.toml

@@ -0,0 +1,25 @@
+[tool.pytest.ini_options]
+addopts = [
+  "--color=yes",
+  "--durations=0",
+  "--strict-markers",
+  "--doctest-modules",
+]
+filterwarnings = [
+  "ignore::DeprecationWarning",
+  "ignore::UserWarning",
+]
+log_cli = "True"
+markers = [
+  "slow: slow tests",
+]
+minversion = "6.0"
+testpaths = "tests/"
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: nocover",
+    "raise NotImplementedError",
+    "raise NotImplementedError()",
+    "if __name__ == .__main__.:",
+]

fspen/requirements.txt

@@ -0,0 +1,24 @@
+# --------- pytorch --------- #
+torch>=2.0.0
+torchvision>=0.15.0
+lightning>=2.0.0
+torchmetrics>=0.11.4
+
+# --------- hydra --------- #
+hydra-core==1.3.2
+hydra-colorlog==1.2.0
+hydra-optuna-sweeper==1.2.0
+
+# --------- loggers --------- #
+# wandb
+# neptune-client
+# mlflow
+# comet-ml
+# aim>=3.16.2  # no lower than 3.16.2, see https://github.com/aimhubio/aim/issues/2550
+
+# --------- others --------- #
+rootutils       # standardizing the project root setup
+pre-commit      # hooks for applying linters on commit
+rich            # beautiful text formatting in terminal
+pytest          # tests
+# sh            # for running bash commands in some tests (linux/macos only)

fspen/scripts/schedule.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+# Schedule execution of many runs
+# Run from root folder with: bash scripts/schedule.sh
+
+python src/train.py trainer.max_epochs=5 logger=csv
+
+python src/train.py trainer.max_epochs=10 logger=csv

fspen/setup.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+from setuptools import find_packages, setup
+
+setup(
+    name="src",
+    version="0.0.1",
+    description="Describe Your Cool Project",
+    author="",
+    author_email="",
+    url="https://github.com/user/project",
+    install_requires=["lightning", "hydra-core"],
+    packages=find_packages(),
+    # use this to customize global commands available in the terminal after installing the package
+    entry_points={
+        "console_scripts": [
+            "train_command = src.train:main",
+            "eval_command = src.eval:main",
+        ]
+    },
+)