app.py

import subprocess
import sys
import shlex
#update the system
subprocess.check_call(["apt-get", "update"])
subprocess.check_call([sys.executable,"-m","pip","install",
                       "torch==2.2.0",
                       "torchaudio==2.2.0"])
subprocess.check_call([sys.executable,"-m","pip","install",
                       "einops", "encodec"])
def install_mamba():
    subprocess.run(shlex.split("pip install https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.4.0/causal_conv1d-1.4.0+cu122torch2.3cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"))
    subprocess.run(shlex.split("pip install https://github.com/state-spaces/mamba/releases/download/v1.2.0.post1/mamba_ssm-1.2.0.post1+cu122torch2.2cxx11abiTRUE-cp310-cp310-linux_x86_64.whl"))

install_mamba()

import torch
import spaces
import tempfile
import soundfile as sf
import gradio as gr
import librosa as lb
import yaml
import numpy as np
import matplotlib.pyplot as plt
from pydub import AudioSegment
from model.cleanmel import CleanMel
from model.vocos.pretrained import Vocos
from model.stft import InputSTFT, TargetMel

DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
def read_audio(file_path):
    assert file_path.endswith(('.wav', '.flac')), "Unsupported audio format. Please upload a .wav, .flac file."
    audio, sample_rate = sf.read(file_path)
    
    if audio.ndim > 1:
        # select the loudest channel if stereo
        audio = audio[:, np.argmax(np.abs(audio).mean(axis=0))]
    if sample_rate != 16000:
        audio = lb.resample(audio, orig_sr=sample_rate, target_sr=16000)
        sample_rate = 16000

    return torch.tensor(audio).float().squeeze().unsqueeze(0)

def stft(audio):
    transform = InputSTFT(
            n_fft=512,
            n_win=512,
            n_hop=128,
            normalize=False,
            center=True,
            onesided=True,
            online=False
        ).eval().to(DEVICE)
    return transform(audio)

def mel_transform(audio, X_norm):
    transform = TargetMel(
        sample_rate=16000,
        n_fft=512,
        n_win=512,
        n_hop=128,
        n_mels=80,
        f_min=0,
        f_max=8000,
        power=2,
        center=True,
        normalize=False,
        onesided=True,
        mel_norm="slaney",
        mel_scale="slaney",
        librosa_mel=True,
        online=False
    ).eval().to(DEVICE)
    return transform(audio, X_norm)

def load_cleanmel(model_name):
    if "S" in model_name:
        model_config = f"./configs/cleanmel_offline_S.yaml"
    else:
        model_config = f"./configs/cleanmel_offline_L.yaml"
    model_config = yaml.safe_load(open(model_config, "r"))["model"]["arch"]["init_args"]
    cleanmel = CleanMel(**model_config)
    cleanmel.load_state_dict(torch.load(f"./ckpts/CleanMel/{model_name}.ckpt", map_location=DEVICE))
    return cleanmel.eval()

def load_vocos():
    vocos = Vocos.from_hparams(config_path="./configs/vocos_offline.yaml")
    vocos = Vocos.from_pretrained(None, model_path=f"./ckpts/Vocos/vocos_offline.pt", model=vocos)
    return vocos.eval()

def get_mrm_pred(Y_hat, x, X_norm):
    X_noisy = mel_transform(x, X_norm)
    Y_hat = Y_hat.squeeze()
    Y_hat = torch.square(Y_hat * (torch.sqrt(X_noisy) + 1e-10))
    return Y_hat

def safe_log(x):           
    return torch.log(torch.clip(x, min=1e-5)) 

def output(y_hat, logMel_hat):
    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp_file:
        sf.write(tmp_file.name, y_hat.squeeze().cpu().numpy(), 16000)
    with tempfile.NamedTemporaryFile(suffix='.npy', delete=False) as tmp_logmel_np_file:
        np.save(tmp_logmel_np_file.name, logMel_hat.squeeze().cpu().numpy())
    logMel_img = logMel_hat.squeeze().cpu().numpy()[::-1, :]
    with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_logmel_img:
        # give a plt figure size according to the logMel shape
        plt.figure(figsize=(logMel_img.shape[1] / 100, logMel_img.shape[0] / 50))
        plt.clf()
        plt.imshow(logMel_img, vmin=-11, cmap="jet")
        plt.tight_layout()
        plt.ylabel("Mel bands")
        plt.xlabel("Time (second)")
        plt.yticks([0, 80], [80, 0])
        dur = y_hat.shape[-1] / 16000
        xticks = [int(x) for x in np.linspace(0, logMel_img.shape[-1], 11)]
        xticks_str = ["{:.1f}".format(x) for x in np.linspace(0, dur, 11)]
        plt.xticks(xticks, xticks_str)
        plt.savefig(tmp_logmel_img.name)
    
    return tmp_file.name, tmp_logmel_img.name, tmp_logmel_np_file.name

@spaces.GPU
@torch.inference_mode()
def enhance_cleanmel_L_mask(audio_path):
    model = load_cleanmel("offline_CleanMel_L_mask").to(DEVICE)
    vocos = load_vocos().to(DEVICE)
    x = read_audio(audio_path).to(DEVICE)
    X, X_norm = stft(x)
    Y_hat = model(X, inference=True)
    MRM_hat = torch.sigmoid(Y_hat)
    Y_hat = get_mrm_pred(MRM_hat, x, X_norm)
    logMel_hat = safe_log(Y_hat)
    y_hat = vocos(logMel_hat, X_norm).clamp(min=-1, max=1)
    return output(y_hat, logMel_hat)

@spaces.GPU
@torch.inference_mode()
def enhance_cleanmel_S_mask(audio_path):
    model = load_cleanmel("offline_CleanMel_S_mask").to(DEVICE)
    vocos = load_vocos().to(DEVICE)
    x = read_audio(audio_path).to(DEVICE)
    X, X_norm = stft(x)
    Y_hat = model(X, inference=True)
    MRM_hat = torch.sigmoid(Y_hat)
    Y_hat = get_mrm_pred(MRM_hat, x, X_norm)
    logMel_hat = safe_log(Y_hat)
    y_hat = vocos(logMel_hat, X_norm).clamp(min=-1, max=1)
    return output(y_hat, logMel_hat)

@spaces.GPU
@torch.inference_mode()
def enhance_cleanmel_L_map(audio_path):
    model = load_cleanmel("offline_CleanMel_L_map").to(DEVICE)
    vocos = load_vocos().to(DEVICE)
    x = read_audio(audio_path).to(DEVICE)
    X, X_norm = stft(x)
    logMel_hat = model(X, inference=True)
    y_hat = vocos(logMel_hat, X_norm).clamp(min=-1, max=1)
    return output(y_hat, logMel_hat)

@spaces.GPU
@torch.inference_mode()
def enhance_cleanmel_S_map(audio_path):
    model = load_cleanmel("offline_CleanMel_S_map").to(DEVICE)
    vocos = load_vocos().to(DEVICE)
    x = read_audio(audio_path).to(DEVICE)
    X, X_norm = stft(x)
    logMel_hat = model(X, inference=True)
    y_hat = vocos(logMel_hat, X_norm).clamp(min=-1, max=1)
    return output(y_hat, logMel_hat)

def reset_everything():
    """Reset all components to initial state"""
    return None, None, None


demo = gr.Blocks()
with gr.Blocks(title="CleanMel Demo") as demo:
    gr.Markdown("## CleanMel Demo")
    gr.Markdown("This demo showcases the CleanMel model for speech enhancement. <br>  \
        Only **.wav** and **.flac** files are supported. <br>  \
        --- <br> \
        The model is running on CPU. Please be patient and wait for the result. <br> \
        Inference time reference: <br> \
            - CleanMel_L: **10 mins** for **10-second** audio <br> \
            - CleanMel_S: **4 mins** for **10-second** audio <br> ")
    
    with gr.Row():
        with gr.Column():
            audio_input = gr.Audio(label="Input Audio", type="filepath", sources="upload")
            audio_input_record = gr.Audio(label="Input Audio (Record)", type="filepath", sources="microphone")
    with gr.Row():
        with gr.Column():
            enhance_button_map_S = gr.Button("Enhance File (offline CleanMel_S_map)")
            enhance_button_mask_S = gr.Button("Enhance File (offline CleanMel_S_mask)")
            enhance_button_map_L = gr.Button("Enhance File (offline CleanMel_L_map)")
            enhance_button_mask_L = gr.Button("Enhance File (offline CleanMel_L_mask)")
            
        with gr.Column():
            enhance_button_map_Sr = gr.Button("Enhance Recorded Audio (offline CleanMel_S_map)")
            enhance_button_mask_Sr = gr.Button("Enhance Recorded Audio (offline CleanMel_S_mask)")
            enhance_button_map_Lr = gr.Button("Enhance Recorded Audio (offline CleanMel_L_map)")
            enhance_button_mask_Lr = gr.Button("Enhance Recorded Audio (offline CleanMel_L_mask)")
    with gr.Row():
            clear_btn = gr.Button(
                "🗑️ Clear All",
                variant="secondary",
                size="lg"
            ) 
    output_audio = gr.Audio(label="Enhanced Audio", type="filepath")
    output_mel = gr.Image(label="Output LogMel Spectrogram", type="filepath", visible=True)
    output_np = gr.File(label="Enhanced LogMel Spec. (.npy)", type="filepath")
    enhance_button_map_L.click(
        enhance_cleanmel_L_map, 
        inputs=audio_input, 
        outputs=[output_audio, output_mel, output_np]
    )
    
    enhance_button_mask_L.click(
        enhance_cleanmel_L_mask, 
        inputs=audio_input, 
        outputs=[output_audio, output_mel, output_np]
    )
    
    enhance_button_map_S.click(
        enhance_cleanmel_S_map, 
        inputs=audio_input, 
        outputs=[output_audio, output_mel, output_np]
    )
    
    enhance_button_mask_S.click(
        enhance_cleanmel_S_mask, 
        inputs=audio_input, 
        outputs=[output_audio, output_mel, output_np]
    )
    enhance_button_map_Lr.click(
        enhance_cleanmel_L_map, 
        inputs=audio_input_record, 
        outputs=[output_audio, output_mel, output_np]
    )
    
    enhance_button_mask_Lr.click(
        enhance_cleanmel_L_mask, 
        inputs=audio_input_record, 
        outputs=[output_audio, output_mel, output_np]
    )
    
    enhance_button_map_Sr.click(
        enhance_cleanmel_S_map,
        inputs=audio_input_record,
        outputs=[output_audio, output_mel, output_np]
    )
    
    enhance_button_mask_Sr.click(
        enhance_cleanmel_S_mask,
        inputs=audio_input_record,
        outputs=[output_audio, output_mel, output_np]
    )
    
    clear_btn.click(
            fn=reset_everything,
            outputs=[output_audio, output_mel, output_np]
    )

demo.launch(debug=False)