audio_foundation_models.py

import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio'))
import matplotlib
import librosa
from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
import torch
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
import re
import uuid
import soundfile
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
import numpy as np
from omegaconf import OmegaConf
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
import cv2
import einops
from einops import repeat
from pytorch_lightning import seed_everything
import random
from ldm.util import instantiate_from_config
from ldm.data.extract_mel_spectrogram import TRANSFORMS_16000
from pathlib import Path
from vocoder.hifigan.modules import VocoderHifigan
from vocoder.bigvgan.models import VocoderBigVGAN
from ldm.models.diffusion.ddim import DDIMSampler
from wav_evaluation.models.CLAPWrapper import CLAPWrapper
from inference.svs.ds_e2e import DiffSingerE2EInfer
from audio_to_text.inference_waveform import AudioCapModel
import whisper
from text_to_speech.TTS_binding import TTSInference
from inference.svs.ds_e2e import DiffSingerE2EInfer
from inference.tts.GenerSpeech import GenerSpeechInfer
from utils.hparams import set_hparams
from utils.hparams import hparams as hp
from utils.os_utils import move_file
import scipy.io.wavfile as wavfile


def prompts(name, description):
    def decorator(func):
        func.name = name
        func.description = description
        return func

    return decorator

def initialize_model(config, ckpt, device):
    config = OmegaConf.load(config)
    model = instantiate_from_config(config.model)
    model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False)

    model = model.to(device)
    model.cond_stage_model.to(model.device)
    model.cond_stage_model.device = model.device
    sampler = DDIMSampler(model)
    return sampler

def initialize_model_inpaint(config, ckpt):
    config = OmegaConf.load(config)
    model = instantiate_from_config(config.model)
    model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)
    print(model.device,device,model.cond_stage_model.device)
    sampler = DDIMSampler(model)
    return sampler
def select_best_audio(prompt,wav_list):
    clap_model = CLAPWrapper('useful_ckpts/CLAP/CLAP_weights_2022.pth','useful_ckpts/CLAP/config.yml',use_cuda=torch.cuda.is_available())
    text_embeddings = clap_model.get_text_embeddings([prompt])
    score_list = []
    for data in wav_list:
        sr,wav = data
        audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav),sr)], resample=True)
        score = clap_model.compute_similarity(audio_embeddings, text_embeddings,use_logit_scale=False).squeeze().cpu().numpy()
        score_list.append(score)
    max_index = np.array(score_list).argmax()
    print(score_list,max_index)
    return wav_list[max_index]


class T2I:
    def __init__(self, device):
        print("Initializing T2I to %s" % device)
        self.device = device
        self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
        self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
        self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device)
        self.pipe.to(device)

    @prompts(name="Generate Image From User Input Text",
             description="useful when you want to generate an image from a user input text and save it to a file. "
                         "like: generate an image of an object or something, or generate an image that includes some objects. "
                         "The input to this tool should be a string, representing the text used to generate image. ")

    def inference(self, text):
        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
        refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"]
        print(f'{text} refined to {refined_text}')
        image = self.pipe(refined_text).images[0]
        image.save(image_filename)
        print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}")
        return image_filename

class ImageCaptioning:
    def __init__(self, device):
        print("Initializing ImageCaptioning to %s" % device)
        self.device = device
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device)


    @prompts(name="Remove Something From The Photo",
             description="useful when you want to remove and object or something from the photo "
                         "from its description or location. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the object need to be removed. ")

    def inference(self, image_path):
        inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device)
        out = self.model.generate(**inputs)
        captions = self.processor.decode(out[0], skip_special_tokens=True)
        return captions

class T2A:
    def __init__(self, device):
        print("Initializing Make-An-Audio to %s" % device)
        self.device = device
        self.sampler = initialize_model('configs/text-to-audio/txt2audio_args.yaml', 'useful_ckpts/ta40multi_epoch=000085.ckpt', device=device) 
        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device)

    @prompts(name="Generate Audio From User Input Text",
             description="useful for when you want to generate an audio "
                         "from a user input text and it saved it to a file."
                         "The input to this tool should be a string, "
                         "representing the text used to generate audio.")

    def txt2audio(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80):
        SAMPLE_RATE = 16000
        prng = np.random.RandomState(seed)
        start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
        uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
        c = self.sampler.model.get_learned_conditioning(n_samples * [text])
        shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8]  # (z_dim, 80//2^x, 848//2^x)
        samples_ddim, _ = self.sampler.sample(S = ddim_steps,
                                            conditioning = c,
                                            batch_size = n_samples,
                                            shape = shape,
                                            verbose = False,
                                            unconditional_guidance_scale = scale,
                                            unconditional_conditioning = uc,
                                            x_T = start_code)

        x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
        x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1]

        wav_list = []
        for idx,spec in enumerate(x_samples_ddim):
            wav = self.vocoder.vocode(spec)
            wav_list.append((SAMPLE_RATE,wav))
        best_wav = select_best_audio(text, wav_list)
        return best_wav

    def inference(self, text, seed = 55, scale = 1.5, ddim_steps = 100, n_samples = 3, W = 624, H = 80):
        melbins,mel_len = 80,624
        with torch.no_grad():
            result = self.txt2audio(
                text = text,
                H = melbins,
                W = mel_len
            )
        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        soundfile.write(audio_filename, result[1], samplerate = 16000)
        print(f"Processed T2I.run, text: {text}, audio_filename: {audio_filename}")
        return audio_filename

class I2A:
    def __init__(self, device):
        print("Initializing Make-An-Audio-Image to %s" % device)
        self.device = device
        self.sampler = initialize_model('text_to_audio/Make_An_Audio_img/configs/img_to_audio/img2audio_args.yaml', 'text_to_audio/Make_An_Audio_img/useful_ckpts/ta54_epoch=000216.ckpt', device=device)
        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio_img/vocoder/logs/bigv16k53w',device=device)

    @prompts(name="Generate Audio From The Image",
             description="useful for when you want to generate an audio "
                         "based on an image.""
                         "The input to this tool should be a string, "
                         "representing the image_path. ")

    def img2audio(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80):
        SAMPLE_RATE = 16000
        n_samples = 1 # only support 1 sample
        prng = np.random.RandomState(seed)
        start_code = prng.randn(n_samples, self.sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)
        uc = self.sampler.model.get_learned_conditioning(n_samples * [""])
        #image = Image.fromarray(image)
        image = Image.open(image)
        image = self.sampler.model.cond_stage_model.preprocess(image).unsqueeze(0)
        image_embedding = self.sampler.model.cond_stage_model.forward_img(image)
        c = image_embedding.repeat(n_samples, 1, 1)# shape:[1,77,1280],即还没有变成句子embedding，仍是每个单词的embedding
        shape = [self.sampler.model.first_stage_model.embed_dim, H//8, W//8]  # (z_dim, 80//2^x, 848//2^x)
        samples_ddim, _ = self.sampler.sample(S=ddim_steps,
                                            conditioning=c,
                                            batch_size=n_samples,
                                            shape=shape,
                                            verbose=False,
                                            unconditional_guidance_scale=scale,
                                            unconditional_conditioning=uc,
                                            x_T=start_code)

        x_samples_ddim = self.sampler.model.decode_first_stage(samples_ddim)
        x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0) # [0, 1]
        wav_list = []
        for idx,spec in enumerate(x_samples_ddim):
            wav = self.vocoder.vocode(spec)
            wav_list.append((SAMPLE_RATE,wav))
        best_wav = wav_list[0]
        return best_wav
    def inference(self, image, seed = 55, scale = 3, ddim_steps = 100, W = 624, H = 80):
        melbins,mel_len = 80,624
        with torch.no_grad():
            result = self.img2audio(
                image=image,
                H=melbins, 
                W=mel_len
            )
        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        soundfile.write(audio_filename, result[1], samplerate = 16000)
        print(f"Processed I2a.run, image_filename: {image}, audio_filename: {audio_filename}")
        return audio_filename

class T2S:
    def __init__(self, device= None):
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        print("Initializing DiffSinger to %s" % device)
        self.device = device
        self.exp_name = 'checkpoints/0831_opencpop_ds1000'
        self.config= 'NeuralSeq/egs/egs_bases/svs/midi/e2e/opencpop/ds1000.yaml'
        self.set_model_hparams()
        self.pipe = DiffSingerE2EInfer(self.hp, device)
        self.default_inp = {
            'text': '你 说 你 不 SP 懂 为 何 在 这 时 牵 手 AP',
            'notes': 'D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | D#4/Eb4 | rest | D#4/Eb4 | D4 | D4 | D4 | D#4/Eb4 | F4 | D#4/Eb4 | D4 | rest',
            'notes_duration': '0.113740 | 0.329060 | 0.287950 | 0.133480 | 0.150900 | 0.484730 | 0.242010 | 0.180820 | 0.343570 | 0.152050 | 0.266720 | 0.280310 | 0.633300 | 0.444590'
        }

    @prompts(name="Generate Singing Voice From User Input Text, Note and Duration Sequence",
             description="useful for when you want to generate a piece of singing voice (Optional: from User Input Text, Note and Duration Sequence) "
                         "and save it to a file.""
                         "If Like: Generate a piece of singing voice, the input to this tool should be \"\" since there is no User Input Text, Note and Duration Sequence. "
                         "If Like: Generate a piece of singing voice. Text: xxx, Note: xxx, Duration: xxx. "
                         "Or Like: Generate a piece of singing voice. Text is xxx, note is xxx, duration is xxx."
                         "The input to this tool should be a comma seperated string of three, "
                         "representing text, note and duration sequence since User Input Text, Note and Duration Sequence are all provided. ")

    def set_model_hparams(self):
        set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False)
        self.hp = hp

    def inference(self, inputs):
        self.set_model_hparams()
        val = inputs.split(",")
        key = ['text', 'notes', 'notes_duration']
        try:
            inp = {k: v for k, v in zip(key, val)}
            wav = self.pipe.infer_once(inp)
        except:
            print('Error occurs. Generate default audio sample.\n')
            inp = self.default_inp
            wav = self.pipe.infer_once(inp)
        wav *= 32767
        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16))
        print(f"Processed T2S.run, audio_filename: {audio_filename}")
        return audio_filename

class TTS_OOD:
    def __init__(self, device):
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        print("Initializing GenerSpeech to %s" % device)
        self.device = device
        self.exp_name = 'checkpoints/GenerSpeech'
        self.config = 'NeuralSeq/modules/GenerSpeech/config/generspeech.yaml'
        self.set_model_hparams()
        self.pipe = GenerSpeechInfer(self.hp, device)

    @prompts(name="Style Transfer",
             description="useful for when you want to generate speech samples with styles "
                         "(e.g., timbre, emotion, and prosody) derived from a reference custom voice. "
                         "Like: Generate a speech with style transferred from this voice. The text is xxx., or speak using the voice of this audio. The text is xxx."
                         "The input to this tool should be a comma seperated string of two, "
                         "representing reference audio path and input text. " )

    def set_model_hparams(self):
        set_hparams(config=self.config, exp_name=self.exp_name, print_hparams=False)
        f0_stats_fn = f'{hp["binary_data_dir"]}/train_f0s_mean_std.npy'
        if os.path.exists(f0_stats_fn):
            hp['f0_mean'], hp['f0_std'] = np.load(f0_stats_fn)
            hp['f0_mean'] = float(hp['f0_mean'])
            hp['f0_std'] = float(hp['f0_std'])
        hp['emotion_encoder_path'] = 'checkpoints/Emotion_encoder.pt'
        self.hp = hp

    def inference(self, inputs):
        self.set_model_hparams()
        key = ['ref_audio', 'text']
        val = inputs.split(",")
        inp = {k: v for k, v in zip(key, val)}
        wav = self.pipe.infer_once(inp)
        wav *= 32767
        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        wavfile.write(audio_filename, self.hp['audio_sample_rate'], wav.astype(np.int16))
        print(
            f"Processed GenerSpeech.run. Input text:{val[1]}. Input reference audio: {val[0]}. Output Audio_filename: {audio_filename}")
        return audio_filename
    
class Inpaint:
    def __init__(self, device):
        print("Initializing Make-An-Audio-inpaint to %s" % device)
        self.device = device
        self.sampler = initialize_model_inpaint('text_to_audio/Make_An_Audio_inpaint/configs/inpaint/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio_inpaint/useful_ckpts/inpaint7_epoch00047.ckpt')
        self.vocoder = VocoderBigVGAN('./vocoder/logs/bigv16k53w',device=device)
        self.cmap_transform = matplotlib.cm.viridis

    @prompts(name="Audio Inpainting",
             description="useful for when you want to inpaint a mel spectrum of an audio and predict this audio, "
                         "this tool will generate a mel spectrum and you can inpaint it, receives audio_path as input. "
                         "The input to this tool should be a string, "
                         "representing the audio_path. " )

    def make_batch_sd(self, mel, mask, num_samples=1):

        mel = torch.from_numpy(mel)[None,None,...].to(dtype=torch.float32)
        mask = torch.from_numpy(mask)[None,None,...].to(dtype=torch.float32)
        masked_mel = (1 - mask) * mel

        mel = mel * 2 - 1
        mask = mask * 2 - 1
        masked_mel = masked_mel * 2 -1

        batch = {
             "mel": repeat(mel.to(device=self.device), "1 ... -> n ...", n=num_samples),
             "mask": repeat(mask.to(device=self.device), "1 ... -> n ...", n=num_samples),
             "masked_mel": repeat(masked_mel.to(device=self.device), "1 ... -> n ...", n=num_samples),
        }
        return batch
    def gen_mel(self, input_audio_path):
        SAMPLE_RATE = 16000
        sr, ori_wav = wavfile.read(input_audio_path)
        print("gen_mel")
        print(sr,ori_wav.shape,ori_wav)
        ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 # order='C'是以C语言格式存储，不用管
        if len(ori_wav.shape)==2:# stereo
            ori_wav = librosa.to_mono(ori_wav.T)# gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len)
        print(sr,ori_wav.shape,ori_wav)
        ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE)

        mel_len,hop_size = 848,256
        input_len = mel_len * hop_size
        if len(ori_wav) < input_len:
            input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0)
        else:
            input_wav = ori_wav[:input_len]
 
        mel = TRANSFORMS_16000(input_wav)
        return mel
    def gen_mel_audio(self, input_audio):
        SAMPLE_RATE = 16000
        sr,ori_wav = input_audio
        print("gen_mel_audio")
        print(sr,ori_wav.shape,ori_wav)

        ori_wav = ori_wav.astype(np.float32, order='C') / 32768.0 # order='C'是以C语言格式存储，不用管
        if len(ori_wav.shape)==2:# stereo
            ori_wav = librosa.to_mono(ori_wav.T)# gradio load wav shape could be (wav_len,2) but librosa expects (2,wav_len)
        print(sr,ori_wav.shape,ori_wav)
        ori_wav = librosa.resample(ori_wav,orig_sr = sr,target_sr = SAMPLE_RATE)

        mel_len,hop_size = 848,256
        input_len = mel_len * hop_size
        if len(ori_wav) < input_len:
            input_wav = np.pad(ori_wav,(0,mel_len*hop_size),constant_values=0)
        else:
            input_wav = ori_wav[:input_len]
        mel = TRANSFORMS_16000(input_wav)
        return mel
    def show_mel_fn(self, input_audio_path):
        crop_len = 500 # the full mel cannot be showed due to gradio's Image bug when using tool='sketch'
        crop_mel = self.gen_mel(input_audio_path)[:,:crop_len]
        color_mel = self.cmap_transform(crop_mel)
        image = Image.fromarray((color_mel*255).astype(np.uint8))
        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
        image.save(image_filename)
        return image_filename
    def inpaint(self, batch, seed, ddim_steps, num_samples=1, W=512, H=512):
        model = self.sampler.model
    
        prng = np.random.RandomState(seed)
        start_code = prng.randn(num_samples, model.first_stage_model.embed_dim, H // 8, W // 8)
        start_code = torch.from_numpy(start_code).to(device=self.device, dtype=torch.float32)

        c = model.get_first_stage_encoding(model.encode_first_stage(batch["masked_mel"]))
        cc = torch.nn.functional.interpolate(batch["mask"],
                                                size=c.shape[-2:])
        c = torch.cat((c, cc), dim=1) # (b,c+1,h,w) 1 is mask

        shape = (c.shape[1]-1,)+c.shape[2:]
        samples_ddim, _ = self.sampler.sample(S=ddim_steps,
                                            conditioning=c,
                                            batch_size=c.shape[0],
                                            shape=shape,
                                            verbose=False)
        x_samples_ddim = model.decode_first_stage(samples_ddim)

    
        mask = batch["mask"]# [-1,1]
        mel = torch.clamp((batch["mel"]+1.0)/2.0,min=0.0, max=1.0)
        mask = torch.clamp((batch["mask"]+1.0)/2.0,min=0.0, max=1.0)
        predicted_mel = torch.clamp((x_samples_ddim+1.0)/2.0,min=0.0, max=1.0)
        inpainted = (1-mask)*mel+mask*predicted_mel
        inpainted = inpainted.cpu().numpy().squeeze()
        inapint_wav = self.vocoder.vocode(inpainted)

        return inpainted, inapint_wav
    def inference(self, input_audio, mel_and_mask, seed = 55, ddim_steps = 100):
        SAMPLE_RATE = 16000
        torch.set_grad_enabled(False)
        mel_img = Image.open(mel_and_mask['image'])
        mask_img = Image.open(mel_and_mask["mask"])
        show_mel = np.array(mel_img.convert("L"))/255 # 由于展示的mel只展示了一部分，所以需要重新从音频生成mel
        mask = np.array(mask_img.convert("L"))/255
        mel_bins,mel_len = 80,848
        input_mel = self.gen_mel_audio(input_audio)[:,:mel_len]# 由于展示的mel只展示了一部分，所以需要重新从音频生成mel
        mask = np.pad(mask,((0,0),(0,mel_len-mask.shape[1])),mode='constant',constant_values=0)# 将mask填充到原来的mel的大小
        print(mask.shape,input_mel.shape)
        with torch.no_grad():
            batch = self.make_batch_sd(input_mel,mask,num_samples=1)
            inpainted,gen_wav = self.inpaint(
                batch=batch,
                seed=seed,
                ddim_steps=ddim_steps,
                num_samples=1,
                H=mel_bins, W=mel_len
            )
        inpainted = inpainted[:,:show_mel.shape[1]]
        color_mel = self.cmap_transform(inpainted)
        input_len = int(input_audio[1].shape[0] * SAMPLE_RATE / input_audio[0])
        gen_wav = (gen_wav * 32768).astype(np.int16)[:input_len]
        image = Image.fromarray((color_mel*255).astype(np.uint8))
        image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
        image.save(image_filename)
        audio_filename = os.path.join('audio', str(uuid.uuid4())[0:8] + ".wav")
        soundfile.write(audio_filename, gen_wav, samplerate = 16000)
        return image_filename, audio_filename
    
class ASR:
    def __init__(self, device):
        print("Initializing Whisper to %s" % device)
        self.device = device
        self.model = whisper.load_model("base", device=device)

    @prompts(name="Transcribe speech",
             description="useful for when you want to know the text corresponding to a human speech, "
                         "receives audio_path as input. "
                         "The input to this tool should be a string, "
                         "representing the audio_path. " )    

    def inference(self, audio_path):
        audio = whisper.load_audio(audio_path)
        audio = whisper.pad_or_trim(audio)
        mel = whisper.log_mel_spectrogram(audio).to(self.device)
        _, probs = self.model.detect_language(mel)
        options = whisper.DecodingOptions()
        result = whisper.decode(self.model, mel, options)
        return result.text

class A2T:
    def __init__(self, device):
        print("Initializing Audio-To-Text Model to %s" % device)
        self.device = device
        self.model = AudioCapModel("audio_to_text/audiocaps_cntrstv_cnn14rnn_trm")

    @prompts(name="Generate Text From The Audio",
             description="useful for when you want to describe an audio in text, "
                         "receives audio_path as input. "
                         "The input to this tool should be a string, "
                         "representing the audio_path. " )    

    def inference(self, audio_path):
        audio = whisper.load_audio(audio_path)
        caption_text = self.model(audio)
        return caption_text[0]