Delete app.py

app.py

@@ -1,106 +0,0 @@
-import gradio as gr
-import json
-import torch
-import wavio
-from tqdm import tqdm
-from huggingface_hub import snapshot_download
-from models import AudioDiffusion, DDPMScheduler
-from audioldm.audio.stft import TacotronSTFT
-from audioldm.variational_autoencoder import AutoencoderKL
-from gradio import Markdown
-
-class Tango:
-    def __init__(self, name="declare-lab/tango", device="cpu"):
-        
-        path = snapshot_download(repo_id=name)
-        
-        vae_config = json.load(open("{}/vae_config.json".format(path)))
-        stft_config = json.load(open("{}/stft_config.json".format(path)))
-        main_config = json.load(open("{}/main_config.json".format(path)))
-        
-        self.vae = AutoencoderKL(**vae_config).to(device)
-        self.stft = TacotronSTFT(**stft_config).to(device)
-        self.model = AudioDiffusion(**main_config).to(device)
-        
-        vae_weights = torch.load("{}/pytorch_model_vae.bin".format(path), map_location=device)
-        stft_weights = torch.load("{}/pytorch_model_stft.bin".format(path), map_location=device)
-        main_weights = torch.load("{}/pytorch_model_main.bin".format(path), map_location=device)
-        
-        self.vae.load_state_dict(vae_weights)
-        self.stft.load_state_dict(stft_weights)
-        self.model.load_state_dict(main_weights)
-
-        print ("Successfully loaded checkpoint from:", name)
-        
-        self.vae.eval()
-        self.stft.eval()
-        self.model.eval()
-        
-        self.scheduler = DDPMScheduler.from_pretrained(main_config["scheduler_name"], subfolder="scheduler")
-        
-    def chunks(self, lst, n):
-        """ Yield successive n-sized chunks from a list. """
-        for i in range(0, len(lst), n):
-            yield lst[i:i + n]
-        
-    def generate(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
-        """ Genrate audio for a single prompt string. """
-        with torch.no_grad():
-            latents = self.model.inference([prompt], self.scheduler, steps, guidance, samples, disable_progress=disable_progress)
-            mel = self.vae.decode_first_stage(latents)
-            wave = self.vae.decode_to_waveform(mel)
-        return wave[0]
-    
-    def generate_for_batch(self, prompts, steps=200, guidance=3, samples=1, batch_size=8, disable_progress=True):
-        """ Genrate audio for a list of prompt strings. """
-        outputs = []
-        for k in tqdm(range(0, len(prompts), batch_size)):
-            batch = prompts[k: k+batch_size]
-            with torch.no_grad():
-                latents = self.model.inference(batch, self.scheduler, steps, guidance, samples, disable_progress=disable_progress)
-                mel = self.vae.decode_first_stage(latents)
-                wave = self.vae.decode_to_waveform(mel)
-                outputs += [item for item in wave]
-        if samples == 1:
-            return outputs
-        else:
-            return list(self.chunks(outputs, samples))
-
-# Initialize Tango model
-tango = Tango()
-
-def gradio_generate(prompt):
-    
-    output_wave = tango.generate(prompt)
-    
-    # Save the output_wave as a temporary WAV file
-    output_filename = "temp_output.wav"
-    wavio.write(output_filename, output_wave, rate=22050, sampwidth=2)
-    
-    return output_filename
-
-# Add the description text box
-description_text = '''
-TANGO is a latent diffusion model (LDM) for text-to-audio (TTA) generation. TANGO can generate realistic audios including human sounds, animal sounds, natural and artificial sounds and sound effects from textual prompts. We use the frozen instruction-tuned LLM Flan-T5 as the text encoder and train a UNet based diffusion model for audio generation. We perform comparably to current state-of-the-art models for TTA across both objective and subjective metrics, despite training the LDM on a 63 times smaller dataset. We release our model, training, inference code, and pre-trained checkpoints for the research community.
-'''
-
-# Define Gradio input and output components
-input_text = gr.inputs.Textbox(lines=2, label="Prompt")
-output_audio = gr.outputs.Audio(label="Generated Audio", type="filepath")
-
-# Create Gradio interface
-gr_interface = gr.Interface(
-    fn=gradio_generate,
-    inputs=input_text,
-    outputs=[output_audio],
-    title="Tango Audio Generator",
-    description="Generate audio using Tango model by providing a text prompt.",
-    allow_flagging=False,
-        examples=[
-        ["A Dog Barking"],
-        ["A loud thunderstorm"],
-    ],
-)
-
-# Launch Gradio app
-gr_interface.launch()