Merge pull request #40 from lmzjms/main

update text_to_audio

audio-chatgpt.py

@@ -4,8 +4,6 @@ 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'))
-sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio_img'))
-sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'text_to_audio/Make_An_Audio_inpaint'))
 sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'audio_detection'))
 sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'mono2binaural'))
 import gradio as gr
@@ -186,7 +184,7 @@ class T2A:
 
     def select_best_audio(self, prompt, wav_list):
         from wav_evaluation.models.CLAPWrapper import CLAPWrapper
-        clap_model = CLAPWrapper('useful_ckpts/CLAP/CLAP_weights_2022.pth', 'useful_ckpts/CLAP/config.yml',
+        clap_model = CLAPWrapper('text_to_audio/Make_An_Audio/useful_ckpts/CLAP/CLAP_weights_2022.pth', 'text_to_audio/Make_An_Audio/useful_ckpts/CLAP/config.yml',
                                  use_cuda=torch.cuda.is_available())
         text_embeddings = clap_model.get_text_embeddings([prompt])
         score_list = []
@@ -217,8 +215,8 @@ class I2A:
     def __init__(self, device):
         print("Initializing Make-An-Audio-Image to %s" % device)
         self.device = device
-        self.sampler = self._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.sampler = self._initialize_model('text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/ta54_epoch=000216.ckpt', device=device)
-        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio_img/vocoder/logs/bigv16k53w',device=device)
+        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device)
 
     def _initialize_model(self, config, ckpt, device):
         config = OmegaConf.load(config)
@@ -421,8 +419,8 @@ class Inpaint:
     def __init__(self, device):
         print("Initializing Make-An-Audio-inpaint to %s" % device)
         self.device = device
-        self.sampler = self._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.sampler = self._initialize_model_inpaint('text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml', 'text_to_audio/Make_An_Audio/useful_ckpts/inpaint7_epoch00047.ckpt')
-        self.vocoder = VocoderBigVGAN('./vocoder/logs/bigv16k53w',device=device)
+        self.vocoder = VocoderBigVGAN('text_to_audio/Make_An_Audio/vocoder/logs/bigv16k53w',device=device)
         self.cmap_transform = matplotlib.cm.viridis
 
     def _initialize_model_inpaint(self, config, ckpt):

download.sh

@@ -8,14 +8,10 @@ wget -P checkpoints/0109_hifigan_bigpopcs_hop128/ -i https://huggingface.co/spac
 wget -P checkpoints/0102_xiaoma_pe/ -i https://huggingface.co/spaces/Silentlin/DiffSinger/blob/main/checkpoints/0102_xiaoma_pe/config.yaml https://huggingface.co/spaces/Silentlin/DiffSinger/resolve/main/checkpoints/0102_xiaoma_pe/model_ckpt_steps_60000.ckpt
 #  Text to audio
 cd text_to_audio
-git clone https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio
-git clone https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_img
-git clone https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_inpaint
 wget -P text_to_audio/Make_An_Audio/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio/resolve/main/useful_ckpts/ta40multi_epoch=000085.ckpt
 wget -P text_to_audio/Make_An_Audio/useful_ckpts/CLAP/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio/resolve/main/useful_ckpts/CLAP/CLAP_weights_2022.pth
-wget -P text_to_audio/Make_An_Audio_img/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_img/resolve/main/useful_ckpts/ta54_epoch=000216.ckpt
+wget -P text_to_audio/Make_An_Audio/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_img/resolve/main/useful_ckpts/ta54_epoch=000216.ckpt
-wget -P text_to_audio/Make_An_Audio_img/useful_ckpts/CLAP/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_img/blob/main/useful_ckpts/CLAP/CLAP_weights_2022.pth
+wget -P text_to_audio/Make_An_Audio/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_inpaint/resolve/main/useful_ckpts/inpaint7_epoch00047.ckpt
-wget -P text_to_audio/Make_An_Audio_inpaint/useful_ckpts/ -i https://huggingface.co/spaces/DiffusionSpeech/Make_An_Audio_inpaint/resolve/main/useful_ckpts/inpaint7_epoch00047.ckpt
 #  Text to speech
 wget -P checkpoints/GenerSpeech/ -i https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/blob/main/checkpoints/GenerSpeech/config.yaml https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/checkpoints/GenerSpeech/model_ckpt_steps_300000.ckpt
 wget -P checkpoints/trainset_hifigan/ -i https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/blob/main/checkpoints/trainset_hifigan/config.yaml https://huggingface.co/spaces/Rongjiehuang/GenerSpeech/resolve/main/checkpoints/trainset_hifigan/model_ckpt_steps_1000000.ckpt

requirements.txt

@@ -1,6 +1,7 @@
 --extra-index-url https://download.pytorch.org/whl/cu113
 accelerate
 addict==2.4.0
+aiofiles
 albumentations==1.3.0
 appdirs==1.4.4
 basicsr==1.4.2
@@ -10,17 +11,23 @@ diffusers
 einops==0.3.0
 espnet
 espnet_model_zoo
+ffmpeg-python
 g2p-en==2.1.0
 google==3.0.0
 gradio
-h5py==2.8.0
+h5py
 imageio==2.9.0
 imageio-ffmpeg==0.4.2
 invisible-watermark>=0.1.5
+jieba
 kornia==0.6
 langchain==0.0.101
 librosa
+loguru
 miditoolkit==0.1.7
+mmcv==1.5.0
+mmdet==2.23.0 
+mmengine==0.7.2
 moviepy==1.0.3
 numpy==1.23.1
 omegaconf==2.1.1
@@ -56,8 +63,9 @@ torchlibrosa
 torchmetrics==0.6.0
 torchvision==0.13.1
 transformers==4.26.1
-typing-extensions==3.10.0.2
+typing-extensions==4.0.0
 uuid==1.30
 webdataset==0.2.5
 webrtcvad==2.0.10
 yapf==0.32.0
+git+https://github.com/openai/CLIP.git

text_to_audio/Make_An_Audio/configs/img_to_audio/img2audio_args.yaml

@@ -0,0 +1,77 @@
+model:
+  base_learning_rate: 1.0e-05
+  target: ldm.models.diffusion.ddpm_audio.LatentDiffusion_audio
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: image
+    cond_stage_key: caption
+    image_size: 32      # unused
+    mel_dim: 10         # 80 // 2^3
+    mel_length: 78     # 624 // 2^3
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_by_std: True
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [10000]
+        cycle_lengths: [10000000000000]
+        f_start: [1.e-6]
+        f_max: [1.]
+        f_min: [ 1.]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.custom_openaimodel.UNetModel
+      params:
+        image_size: 32 # ununsed
+        in_channels: 4
+        out_channels: 4
+        model_channels: 256
+        attention_resolutions:
+        - 1
+        - 2
+        num_res_blocks: 2
+        channel_mult:  # num_down = len(ch_mult)-1
+        - 1
+        - 2
+        num_head_channels: 32
+        use_spatial_transformer: true
+        transformer_depth: 1
+        context_dim: 1024
+        use_context_project: false
+
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 848
+          in_channels: 1
+          out_ch: 1
+          ch: 128
+          ch_mult: [ 1, 2, 2, 4 ]  # num_down = len(ch_mult)-1
+          num_res_blocks: 2
+          attn_resolutions: [106, 212]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenGlobalNormOpenCLIPEmbedder
+      params:
+        freeze: True
+        delvisual: False
+
+

text_to_audio/Make_An_Audio/configs/inpaint/txt2audio_args.yaml

@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-05
+  target: ldm.models.diffusion.ddpm_audio.LatentDiffusion_audio
+  params:
+    linear_start: 0.0015
+    linear_end: 0.0205
+    log_every_t: 100
+    timesteps: 1000
+    loss_type: l1
+    first_stage_key: image
+    cond_stage_key: masked_image
+    image_size: 32      # unused
+    mel_dim: 10         # 80 // 2^3
+    mel_length: 106     # 848 // 2^3
+    channels: 4
+    concat_mode: true
+    monitor: val/loss
+    use_ema: False
+
+    scheduler_config:
+      target: ldm.lr_scheduler.LambdaWarmUpCosineScheduler
+      params:
+        verbosity_interval: 0
+        warm_up_steps: 1000
+        max_decay_steps: 50000
+        lr_start: 0.001
+        lr_max: 0.1
+        lr_min: 0.0001
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # ununsed
+        in_channels: 9 # 4 + 1 + 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions:
+        - 1
+        - 2
+        num_res_blocks: 2
+        channel_mult:  # num_down = len(ch_mult)-1
+        - 1
+        - 2
+        num_heads: 8
+        resblock_updown: true
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ckpt_path: # /apdcephfs/share_1316500/nlphuang/results/Text_to_audio/ae15/2022-12-15T22-24-00_mixdata_kl_4_tile/epoch=000009-v2.ckpt
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 848
+          in_channels: 1
+          out_ch: 1
+          ch: 128
+          ch_mult: [ 1, 2, 2, 4 ]  # num_down = len(ch_mult)-1
+          num_res_blocks: 2
+          attn_resolutions: [106, 212]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config: __is_first_stage__
+

text_to_audio/Make_An_Audio/ldm/data/extract_mel_spectrogram.py

@@ -0,0 +1,151 @@
+import argparse
+import os
+import os.path as P
+from copy import deepcopy
+from functools import partial
+from glob import glob
+from multiprocessing import Pool
+from pathlib import Path
+
+import librosa
+import numpy as np
+import torchvision
+
+
+class MelSpectrogram(object):
+    def __init__(self, sr, nfft, fmin, fmax, nmels, hoplen, spec_power, inverse=False):
+        self.sr = sr
+        self.nfft = nfft
+        self.fmin = fmin
+        self.fmax = fmax
+        self.nmels = nmels
+        self.hoplen = hoplen
+        self.spec_power = spec_power
+        self.inverse = inverse
+
+        self.mel_basis = librosa.filters.mel(sr=sr, n_fft=nfft, fmin=fmin, fmax=fmax, n_mels=nmels)
+
+    def __call__(self, x):
+        if self.inverse:
+            spec = librosa.feature.inverse.mel_to_stft(
+                x, sr=self.sr, n_fft=self.nfft, fmin=self.fmin, fmax=self.fmax, power=self.spec_power
+            )
+            wav = librosa.griffinlim(spec, hop_length=self.hoplen)
+            return wav
+        else:
+            spec = np.abs(librosa.stft(x, n_fft=self.nfft, hop_length=self.hoplen)) ** self.spec_power
+            mel_spec = np.dot(self.mel_basis, spec)
+            return mel_spec
+
+class LowerThresh(object):
+    def __init__(self, min_val, inverse=False):
+        self.min_val = min_val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return np.maximum(self.min_val, x)
+
+class Add(object):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x - self.val
+        else:
+            return x + self.val
+
+class Subtract(Add):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x + self.val
+        else:
+            return x - self.val
+
+class Multiply(object):
+    def __init__(self, val, inverse=False) -> None:
+        self.val = val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x / self.val
+        else:
+            return x * self.val
+
+class Divide(Multiply):
+    def __init__(self, val, inverse=False):
+        self.inverse = inverse
+        self.val = val
+
+    def __call__(self, x):
+        if self.inverse:
+            return x * self.val
+        else:
+            return x / self.val
+
+class Log10(object):
+    def __init__(self, inverse=False):
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return 10 ** x
+        else:
+            return np.log10(x)
+
+class Clip(object):
+    def __init__(self, min_val, max_val, inverse=False):
+        self.min_val = min_val
+        self.max_val = max_val
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return np.clip(x, self.min_val, self.max_val)
+
+class TrimSpec(object):
+    def __init__(self, max_len, inverse=False):
+        self.max_len = max_len
+        self.inverse = inverse
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return x[:, :self.max_len]
+
+class MaxNorm(object):
+    def __init__(self, inverse=False):
+        self.inverse = inverse
+        self.eps = 1e-10
+
+    def __call__(self, x):
+        if self.inverse:
+            return x
+        else:
+            return x / (x.max() + self.eps)
+
+
+TRANSFORMS_16000 = torchvision.transforms.Compose([
+    MelSpectrogram(sr=16000, nfft=1024, fmin=125, fmax=7600, nmels=80, hoplen=1024//4, spec_power=1),
+    LowerThresh(1e-5),
+    Log10(),
+    Multiply(20),
+    Subtract(20),
+    Add(100),
+    Divide(100),
+    Clip(0, 1.0)
+    # TrimSpec(860)
+])
+

text_to_audio/Make_An_Audio/ldm/modules/encoders/modules.py

@@ -9,7 +9,7 @@ from importlib_resources import files
 from ldm.modules.encoders.CLAP.utils import read_config_as_args
 from ldm.modules.encoders.CLAP.clap import TextEncoder
 from ldm.util import default, count_params
-
+import open_clip
 
 class AbstractEncoder(nn.Module):
     def __init__(self):
@@ -312,3 +312,39 @@ class FrozenFLANEmbedder(AbstractEncoder):
 
     def encode(self, text):
         return self(text)
+class FrozenGlobalNormOpenCLIPEmbedder(AbstractEncoder):
+    """
+    Uses the OpenCLIP transformer encoder for text
+    """
+    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", freeze=True, delvisual=True):
+        super().__init__()
+        model, _, preprocess = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+        if delvisual:
+            del model.visual
+            del preprocess
+        else:
+            self.preprocess = preprocess
+        self.model = model
+
+        self.device = device
+        if freeze:
+            self.freeze()
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = open_clip.tokenize(text)
+        z = self.model.encode_text(tokens.to(self.device))
+        z /= z.norm(dim=-1, keepdim=True)
+        return z.unsqueeze(1)
+
+    def forward_img(self, image):
+        z = self.model.encode_image(image.to(self.device))
+        z /= z.norm(dim=-1, keepdim=True)
+        return z.unsqueeze(1)
+
+    def encode(self, text):
+        return self(text)