Delete app.py.py

app.py.py

@@ -1,236 +0,0 @@
-import os
-import torch
-import argparse
-import numpy as np
-from scipy.io.wavfile import write
-import torchaudio
-import utils
-from Mels_preprocess import MelSpectrogramFixed
-
-from hierspeechpp_speechsynthesizer import (
-    SynthesizerTrn
-)
-from ttv_v1.text import text_to_sequence
-from ttv_v1.t2w2v_transformer import SynthesizerTrn as Text2W2V
-from speechsr24k.speechsr import SynthesizerTrn as SpeechSR24
-from speechsr48k.speechsr import SynthesizerTrn as SpeechSR48
-from denoiser.generator import MPNet
-from denoiser.infer import denoise
-
-import gradio as gr
-
-def load_text(fp):
-    with open(fp, 'r') as f:
-        filelist = [line.strip() for line in f.readlines()]
-    return filelist
-def load_checkpoint(filepath, device):
-    print(filepath)
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-def get_param_num(model):
-    num_param = sum(param.numel() for param in model.parameters())
-    return num_param
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-def add_blank_token(text):
-
-    text_norm = intersperse(text, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-def tts(text, 
-        prompt, 
-        ttv_temperature, 
-        vc_temperature, 
-        duratuion_temperature, 
-        duratuion_length, 
-        denoise_ratio, 
-        random_seed):
-    
-    torch.manual_seed(random_seed)
-    torch.cuda.manual_seed(random_seed)
-    np.random.seed(random_seed)
-
-    text_len = len(text)
-    if text_len > 200:
-        raise gr.Error("Text length limited to 200 characters for this demo. Current text length is " + str(text_len))
-       
-    else:
-        text = text_to_sequence(str(text), ["english_cleaners2"])
-        
-        token = add_blank_token(text).unsqueeze(0).cuda()
-        token_length = torch.LongTensor([token.size(-1)]).cuda() 
-
-        # Prompt load
-        # sample_rate, audio = prompt
-        # audio = torch.FloatTensor([audio]).cuda()
-        # if audio.shape[0] != 1:
-        #     audio = audio[:1,:] 
-        # audio = audio / 32768 
-        audio, sample_rate = torchaudio.load(prompt)
-
-        # support only single channel
-
-        # Resampling
-        if sample_rate != 16000:
-            audio = torchaudio.functional.resample(audio, sample_rate, 16000, resampling_method="kaiser_window") 
-
-        # We utilize a hop size of 320 but denoiser uses a hop size of 400 so we utilize a hop size of 1600
-        ori_prompt_len = audio.shape[-1]
-        p = (ori_prompt_len // 1600 + 1) * 1600 - ori_prompt_len
-        audio = torch.nn.functional.pad(audio, (0, p), mode='constant').data
-
-        # If you have a memory issue during denosing the prompt, try to denoise the prompt with cpu before TTS 
-        # We will have a plan to replace a memory-efficient denoiser 
-        if denoise == 0:
-            audio = torch.cat([audio.cuda(), audio.cuda()], dim=0)
-        else:
-            with torch.no_grad():
-                
-                if ori_prompt_len > 80000:
-                    denoised_audio = []
-                    for i in range((ori_prompt_len//80000)):
-                        denoised_audio.append(denoise(audio.squeeze(0).cuda()[i*80000:(i+1)*80000], denoiser, hps_denoiser))
-                    
-                    denoised_audio.append(denoise(audio.squeeze(0).cuda()[(i+1)*80000:], denoiser, hps_denoiser))
-                    denoised_audio = torch.cat(denoised_audio, dim=1)
-                else:
-                    denoised_audio = denoise(audio.squeeze(0).cuda(), denoiser, hps_denoiser)
-
-            audio = torch.cat([audio.cuda(), denoised_audio[:,:audio.shape[-1]]], dim=0)
-
-        audio = audio[:,:ori_prompt_len]  # 20231108 We found that large size of padding decreases a performance so we remove the paddings after denosing.
-
-        if audio.shape[-1]<48000:
-            audio = torch.cat([audio,audio,audio,audio,audio], dim=1)
-
-        src_mel = mel_fn(audio.cuda())
-
-        src_length = torch.LongTensor([src_mel.size(2)]).to(device)
-        src_length2 = torch.cat([src_length,src_length], dim=0)
-
-        ## TTV (Text --> W2V, F0)
-        with torch.no_grad():
-            w2v_x, pitch = text2w2v.infer_noise_control(token, token_length, src_mel, src_length2, 
-                                                        noise_scale=ttv_temperature, noise_scale_w=duratuion_temperature, 
-                                                        length_scale=duratuion_length, denoise_ratio=denoise_ratio)
-            src_length = torch.LongTensor([w2v_x.size(2)]).cuda()  
-        
-            pitch[pitch<torch.log(torch.tensor([55]).cuda())]  = 0
-
-            ## Hierarchical Speech Synthesizer (W2V, F0 --> 16k Audio)
-            converted_audio = \
-                net_g.voice_conversion_noise_control(w2v_x, src_length, src_mel, src_length2, pitch, noise_scale=vc_temperature, denoise_ratio=denoise_ratio)
-    
-            converted_audio = speechsr(converted_audio)
-
-        converted_audio = converted_audio.squeeze()
-
-        converted_audio = converted_audio / (torch.abs(converted_audio).max()) * 32767.0 * 0.999 
-        converted_audio = converted_audio.cpu().numpy().astype('int16')
-
-        write('output.wav', 48000, converted_audio)
-        return 'output.wav'
-
-def main():
-    print('Initializing Inference Process..')
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--input_prompt', default='example/steve-jobs-2005.wav')
-    parser.add_argument('--input_txt', default='example/abstract.txt')
-    parser.add_argument('--output_dir', default='output')
-    parser.add_argument('--ckpt', default='./logs/hierspeechpp_eng_kor/hierspeechpp_v2_ckpt.pth')
-    parser.add_argument('--ckpt_text2w2v', '-ct', help='text2w2v checkpoint path', default='./logs/ttv_libritts_v1/ttv_lt960_ckpt.pth')
-    parser.add_argument('--ckpt_sr', type=str, default='./speechsr24k/G_340000.pth')  
-    parser.add_argument('--ckpt_sr48', type=str, default='./speechsr48k/G_100000.pth')  
-    parser.add_argument('--denoiser_ckpt', type=str, default='denoiser/g_best')
-    parser.add_argument('--scale_norm', type=str, default='max')
-    parser.add_argument('--output_sr', type=float, default=48000)
-    parser.add_argument('--noise_scale_ttv', type=float,
-                        default=0.333)
-    parser.add_argument('--noise_scale_vc', type=float,
-                        default=0.333)
-    parser.add_argument('--denoise_ratio', type=float,
-                        default=0.8)
-    parser.add_argument('--duration_ratio', type=float,
-                        default=0.8)
-    parser.add_argument('--seed', type=int,
-                        default=1111)
-    a = parser.parse_args()
-
-    global device, hps, hps_t2w2v,h_sr,h_sr48, hps_denoiser
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    hps = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt)[0], 'config.json'))
-    hps_t2w2v = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_text2w2v)[0], 'config.json'))
-    h_sr = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr)[0], 'config.json') )
-    h_sr48 = utils.get_hparams_from_file(os.path.join(os.path.split(a.ckpt_sr48)[0], 'config.json') )
-    hps_denoiser = utils.get_hparams_from_file(os.path.join(os.path.split(a.denoiser_ckpt)[0], 'config.json'))
-
-    global mel_fn, net_g, text2w2v, speechsr, denoiser
-
-    mel_fn = MelSpectrogramFixed(
-        sample_rate=hps.data.sampling_rate,
-        n_fft=hps.data.filter_length,
-        win_length=hps.data.win_length,
-        hop_length=hps.data.hop_length,
-        f_min=hps.data.mel_fmin,
-        f_max=hps.data.mel_fmax,
-        n_mels=hps.data.n_mel_channels,
-        window_fn=torch.hann_window
-    ).cuda()  
-
-    net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        **hps.model).cuda()
-    net_g.load_state_dict(torch.load(a.ckpt))
-    _ = net_g.eval()
-
-    text2w2v = Text2W2V(hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    **hps_t2w2v.model).cuda()
-    text2w2v.load_state_dict(torch.load(a.ckpt_text2w2v))
-    text2w2v.eval()
-  
-    speechsr = SpeechSR48(h_sr48.data.n_mel_channels,
-        h_sr48.train.segment_size // h_sr48.data.hop_length,
-        **h_sr48.model).cuda()
-    utils.load_checkpoint(a.ckpt_sr48, speechsr, None)
-    speechsr.eval()
-           
-    denoiser = MPNet(hps_denoiser).cuda()
-    state_dict = load_checkpoint(a.denoiser_ckpt, device)
-    denoiser.load_state_dict(state_dict['generator'])
-    denoiser.eval()
-
-    demo_play = gr.Interface(fn = tts,
-                     inputs = [gr.Textbox(max_lines=6, label="Input Text", value="HierSpeech is a zero shot speech synthesis model, which can generate high-quality audio", info="Up to 200 characters"), 
-                               gr.Audio(type='filepath', value="./example/3_rick_gt.wav"), 
-                               gr.Slider(0,1,0.333), 
-                               gr.Slider(0,1,0.333), 
-                               gr.Slider(0,1,1.0), 
-                               gr.Slider(0.5,2,1.0), 
-                               gr.Slider(0,1,0), 
-                               gr.Slider(0,9999,1111)],
-                     outputs = 'audio', 
-                     title = 'HierSpeech++',
-                     description  =  '''<div>
-                            <p style="text-align: left"> HierSpeech++ is a zero-shot speech synthesis model.</p>
-                            <p style="text-align: left"> Our model is trained with LibriTTS dataset so this model only supports english. We will release a multi-lingual HierSpeech++ soon.</p>
-                            <p style="text-align: left"> <a href="https://sh-lee-prml.github.io/HierSpeechpp-demo/">[Demo Page]</a> <a href="https://github.com/sh-lee-prml/HierSpeechpp">[Source Code]</a></p>
-                        </div>''',                      
-                     examples=[["HierSpeech is a zero-shot speech synthesis model, which can generate high-quality audio", "./example/3_rick_gt.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
-                                ["HierSpeech is a zero-shot speech synthesis model, which can generate high-quality audio", "./example/ex01_whisper_00359.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
-                               ["Hi there, I'm your new voice clone. Try your best to upload quality audio", "./example/female.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
-                               ["Hello I'm HierSpeech++", "./example/reference_1.wav", 0.333,0.333, 1.0, 1.0, 0, 1111],
-                               ]
-                    )
-    demo_play.launch(share=True, server_port=8888)
-
-if __name__ == '__main__':
-    main()