app.py

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 pydub import AudioSegment
from gradio import Markdown
import spaces

import torch
#from diffusers.models.autoencoder_kl import AutoencoderKL
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from diffusers import DiffusionPipeline,AudioPipelineOutput
from transformers import CLIPTextModel, T5EncoderModel, AutoModel, T5Tokenizer, T5TokenizerFast
from typing import Union
from diffusers.utils.torch_utils import randn_tensor
from tqdm import tqdm





class Tango2Pipeline(DiffusionPipeline):

    
    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: T5EncoderModel,
        tokenizer: Union[T5Tokenizer, T5TokenizerFast],
        unet: UNet2DConditionModel,
        scheduler: DDPMScheduler
    ):
        
        super().__init__()
    
        self.register_modules(vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        scheduler=scheduler
        )
        
    
    def _encode_prompt(self, prompt):
        device = self.text_encoder.device
        
        batch = self.tokenizer(
            prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
        )
        input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)

       
        encoder_hidden_states = self.text_encoder(
                input_ids=input_ids, attention_mask=attention_mask
            )[0]

        boolean_encoder_mask = (attention_mask == 1).to(device)
        
        return encoder_hidden_states, boolean_encoder_mask
        
    def _encode_text_classifier_free(self, prompt, num_samples_per_prompt):
        device = self.text_encoder.device
        batch = self.tokenizer(
            prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
        )
        input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)

        with torch.no_grad():
            prompt_embeds = self.text_encoder(
                input_ids=input_ids, attention_mask=attention_mask
            )[0]
                
        prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
        attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)

        # get unconditional embeddings for classifier free guidance
        uncond_tokens = [""] * len(prompt)

        max_length = prompt_embeds.shape[1]
        uncond_batch = self.tokenizer(
            uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
        )
        uncond_input_ids = uncond_batch.input_ids.to(device)
        uncond_attention_mask = uncond_batch.attention_mask.to(device)

        with torch.no_grad():
            negative_prompt_embeds = self.text_encoder(
                input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
            )[0]
                
        negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
        uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)

        # For classifier free guidance, we need to do two forward passes.
        # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
        prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
        boolean_prompt_mask = (prompt_mask == 1).to(device)

        return prompt_embeds, boolean_prompt_mask
        
    def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
        shape = (batch_size, num_channels_latents, 256, 16)
        latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * inference_scheduler.init_noise_sigma
        return latents
    
    @torch.no_grad()
    def inference(self, prompt, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
                  disable_progress=True):
        device = self.text_encoder.device
        classifier_free_guidance = guidance_scale > 1.0
        batch_size = len(prompt) * num_samples_per_prompt

        if classifier_free_guidance:
            prompt_embeds, boolean_prompt_mask = self._encode_text_classifier_free(prompt, num_samples_per_prompt)
        else:
            prompt_embeds, boolean_prompt_mask = self._encode_text(prompt)
            prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
            boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)

        inference_scheduler.set_timesteps(num_steps, device=device)
        timesteps = inference_scheduler.timesteps

        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)

        num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
        progress_bar = tqdm(range(num_steps), disable=disable_progress)

        for i, t in enumerate(timesteps):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
            latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)

            noise_pred = self.unet(
                latent_model_input, t, encoder_hidden_states=prompt_embeds,
                encoder_attention_mask=boolean_prompt_mask
            ).sample

            # perform guidance
            if classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            latents = inference_scheduler.step(noise_pred, t, latents).prev_sample

            # call the callback, if provided
            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
                progress_bar.update(1)

        return latents
        
    @torch.no_grad()
    def __call__(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
        """ Genrate audio for a single prompt string. """
        with torch.no_grad():
            latents = self.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 AudioPipelineOutput(audios=wave)


# Automatic device detection
if torch.cuda.is_available():
    device_type = "cuda"
    device_selection = "cuda:0"
else:
    device_type = "cpu"
    device_selection = "cpu"

class Tango:
    def __init__(self, name="declare-lab/tango2", device=device_selection):
        
        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

tango = Tango(device="cpu")
tango.vae.to(device_type)
tango.stft.to(device_type)
tango.model.to(device_type)

pipe = Tango2Pipeline(vae=tango.vae,
                      text_encoder=tango.model.text_encoder,
                      tokenizer=tango.model.tokenizer,
                      unet=tango.model.unet,
                      scheduler=tango.scheduler
                      )

    
@spaces.GPU(duration=60)
def gradio_generate(prompt, output_format, steps, guidance):
    output_wave = pipe(prompt,steps,guidance) ## Using pipeliine automatically uses flash attention for torch2.0 above
    #output_wave = tango.generate(prompt, steps, guidance)
    # output_filename = f"{prompt.replace(' ', '_')}_{steps}_{guidance}"[:250] + ".wav"
    output_wave = output_wave.audios[0]
    output_filename = "temp.wav"
    wavio.write(output_filename, output_wave, rate=16000, sampwidth=2)

    if (output_format == "mp3"):
        AudioSegment.from_wav("temp.wav").export("temp.mp3", format = "mp3")
        output_filename = "temp.mp3"

    return output_filename

# description_text = """
# <p><a href="https://huggingface.co/spaces/declare-lab/tango/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
# Generate audio using TANGO by providing a text prompt.
# <br/><br/>Limitations: TANGO is trained on the small AudioCaps dataset so it may not generate good audio \
# samples related to concepts that it has not seen in training (e.g. singing). For the same reason, TANGO \
# is not always able to finely control its generations over textual control prompts. For example, \
# the generations from TANGO for prompts Chopping tomatoes on a wooden table and Chopping potatoes \
# on a metal table are very similar. \
# <br/><br/>We are currently training another version of TANGO on larger datasets to enhance its generalization, \
# compositional and controllable generation ability.
# <br/><br/>We recommend using a guidance scale of 3. The default number of steps is set to 100. More steps generally lead to better quality of generated audios but will take longer.
# <br/><br/>
# <h1> ChatGPT-enhanced audio generation</h1>
# <br/>
# As TANGO consists of an instruction-tuned LLM, it is able to process complex sound descriptions allowing us to provide more detailed instructions to improve the generation quality.
# For example, ``A boat is moving on the sea'' vs ``The sound of the water lapping against the hull of the boat or splashing as you move through the waves''. The latter is obtained by prompting ChatGPT to explain the sound generated when a boat moves on the sea.
# Using this ChatGPT-generated description of the sound, TANGO provides superior results.
# <p/>
# """
description_text = """
<p><a href="https://huggingface.co/spaces/declare-lab/tango2/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
Generate audio using Tango2 by providing a text prompt. Tango2 was built from Tango and was trained on <a href="https://huggingface.co/datasets/declare-lab/audio-alpaca">Audio-alpaca</a>
<br/><br/> This is the demo for Tango2 for text to audio generation: <a href="https://arxiv.org/abs/2404.09956">Read our paper.</a>
<p/>
"""
# Gradio input and output components
input_text = gr.Textbox(lines=2, label="Prompt")
output_format = gr.Radio(label = "Output format", info = "The file you can dowload", choices = ["mp3", "wav"], value = "wav")
output_audio = gr.Audio(label="Generated Audio", type="filepath")
denoising_steps = gr.Slider(minimum=100, maximum=200, value=100, step=1, label="Steps", interactive=True)
guidance_scale = gr.Slider(minimum=1, maximum=10, value=3, step=0.1, label="Guidance Scale", interactive=True)

# Gradio interface
gr_interface = gr.Interface(
    fn=gradio_generate,
    inputs=[input_text, output_format, denoising_steps, guidance_scale],
    outputs=[output_audio],
    title="Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization",
    description=description_text,
    allow_flagging=False,
    examples=[
        ["Quiet speech and then and airplane flying away"],
        ["A bicycle peddling on dirt and gravel followed by a man speaking then laughing"],
        ["Ducks quack and water splashes with some animal screeching in the background"],
        ["Describe the sound of the ocean"],
        ["A woman and a baby are having a conversation"],
        ["A man speaks followed by a popping noise and laughter"],
        ["A cup is filled from a faucet"],
        ["An audience cheering and clapping"],
        ["Rolling thunder with lightning strikes"],
        ["A dog barking and a cat mewing and a racing car passes by"],
        ["Gentle water stream, birds chirping and sudden gun shot"],
        ["A man talking followed by a goat baaing then a metal gate sliding shut as ducks quack and wind blows into a microphone."],
        ["A dog barking"],
        ["A cat meowing"],
        ["Wooden table tapping sound while water pouring"],
        ["Applause from a crowd with distant clicking and a man speaking over a loudspeaker"],
        ["two gunshots followed by birds flying away while chirping"],
        ["Whistling with birds chirping"],
        ["A person snoring"],
        ["Motor vehicles are driving with loud engines and a person whistles"],
        ["People cheering in a stadium while thunder and lightning strikes"],
        ["A helicopter is in flight"],
        ["A dog barking and a man talking and a racing car passes by"],
    ],
    cache_examples="lazy", # Turn on to cache.
)

# Launch Gradio app
gr_interface.queue(10).launch()