pip package

.gitignore

-~__pycache__/
+__pycache__/
 *.py[cod]
 *$py.class
 
@@ -168,3 +168,8 @@ cython_debug/
 
 # PyPI configuration file
 .pypirc
+
+
+.DS_Store
+
+*.wav

README.md

-<h1 align="center">✨ 
-<br/>  
-TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization 
-<br/>
-✨✨✨
-
-
-</h1>
+# TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization 
 
 <div align="center">
   <img src="assets/tf_teaser.png" alt="TangoFlux" width="1000" />
 
 <br/>
 
-[![arXiv](https://img.shields.io/badge/Read_the_Paper-blue?link=https%3A%2F%2Fopenreview.net%2Fattachment%3Fid%3DtpJPlFTyxd%26name%3Dpdf)](https://arxiv.org/abs/2412.21037) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface-violet?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/Demos-declare--lab-brightred?style=flat)](https://tangoflux.github.io/) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface_Space-8A2BE2?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/spaces/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/TangoFlux_Dataset-Huggingface-red?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/datasets/declare-lab/CRPO) [![Replicate](https://replicate.com/chenxwh/tangoflux/badge)](https://replicate.com/chenxwh/tangoflux)
-
-
-
-
+[![arXiv](https://img.shields.io/badge/Read_the_Paper-blue?link=https%3A%2F%2Fopenreview.net%2Fattachment%3Fid%3DtpJPlFTyxd%26name%3Dpdf)](https://arxiv.org/abs/2412.21037) [![Static Badge](https://img.shields.io/badge/TangoFlux-Huggingface-violet?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/Demos-declare--lab-brightred?style=flat)](https://tangoflux.github.io/) [![Static Badge](https://img.shields.io/badge/TangoFlux-Hugging_Face_Space-8A2BE2?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/spaces/declare-lab/TangoFlux) [![Static Badge](https://img.shields.io/badge/TangoFlux_Dataset-Huggingface-red?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/datasets/declare-lab/CRPO) [![Replicate](https://replicate.com/chenxwh/tangoflux/badge)](https://replicate.com/chenxwh/tangoflux)
 
 </div>
 
-## Quickstart on Google Colab
+## Demos
+
+[![Hugging Face Space](https://img.shields.io/badge/Hugging_Face_Space-TangoFlux-blue?logo=huggingface&link=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fdeclare-lab%2FTangoFlux)](https://huggingface.co/spaces/declare-lab/TangoFlux)
 
-| Colab |
-| --- |
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1j__4fl_BlaVS_225M34d-EKxsVDJPRiR?usp=sharing)
 
 ## Overall Pipeline
+
 TangoFlux consists of FluxTransformer blocks, which are Diffusion Transformers (DiT) and Multimodal Diffusion Transformers (MMDiT) conditioned on a textual prompt and a duration embedding to generate a 44.1kHz audio up to 30 seconds long. TangoFlux learns a rectified flow trajectory to an audio latent representation encoded by a variational autoencoder (VAE). TangoFlux training pipeline consists of three stages: pre-training, fine-tuning, and preference optimization with CRPO. CRPO, particularly, iteratively generates new synthetic data and constructs preference pairs for preference optimization using DPO loss for flow matching.
 
 ![cover-photo](assets/tangoflux.png)
 
+🚀 **TangoFlux can generate 44.1kHz stereo audio up to 30 seconds in ~3 seconds on a single A40 GPU.**
 
-🚀 **TangoFlux can generate up to 30 seconds long 44.1kHz stereo audios in about 3 seconds.**
+## Installation
 
-## Training TangoFlux
-We use the accelerate package from HuggingFace for multi-gpu training. Run accelerate config from terminal and set up your run configuration by the answering the questions asked. We have placed the default accelerator config in the `configs` folder. Please specify the path to your training files in the configs/tangoflux_config.yaml. A sample of train.json and val.json has been provided. Replace them with your own audio.
+```bash
+pip install tangoflux
+```
+
+## Inference
+
+TangoFlux can generate audio up to 30 seconds long. You must pass a duration to the `model.generate` function when using the Python API. Please note that duration should be between 1 and 30.
+
+### Web Interface
+
+Run the following command to start the web interface:
 
-`tangoflux_config.yaml` defines the training file paths and model hyperparameters:
 ```bash
-CUDA_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' src/train.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
+tangoflux-demo
 ```
 
-To perform DPO training, modify the training files such that each data point contains a "chosen","reject","caption" and "duration". Please specify the path to your training files in the configs/tangoflux_config.yaml. An example has been provided in train_dpo.json. Replace them with your own audio.
+### CLI
+
+Use the CLI to generate audio from text.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' src/train_dpo.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
+tangoflux "Hammer slowly hitting the wooden table" output.wav --duration 10 --steps 50
 ```
-## Inference with TangoFlux
-Download the TangoFlux model and generate audio from a text prompt.
-TangoFlux can generate audios up to 30 second long through passing in a duration variable in the `model.generate` function. Please note that duration should be strictly greather than 1 and lesser than 30.
+
+### Python API
+
 ```python
 import torchaudio
 from tangoflux import TangoFluxInference
-from IPython.display import Audio
 
 model = TangoFluxInference(name='declare-lab/TangoFlux')
 audio = model.generate('Hammer slowly hitting the wooden table', steps=50, duration=10)
 
-Audio(data=audio, rate=44100)
+torchaudio.save('output.wav', audio, 44100)
+```
+
+Our evaluation shows that inference with 50 steps yields the best results. A CFG scale of 3.5, 4, and 4.5 yield similar quality output. Inference with 25 steps yields similar audio quality at a faster speed.
+
+## Training
+
+We use the `accelerate` package from Hugging Face for multi-GPU training. Run `accelerate config` to setup your run configuration. The default accelerate config is in the `configs` folder. Please specify the path to your training files in the `configs/tangoflux_config.yaml`. Samples of `train.json` and `val.json` have been provided. Replace them with your own audio.
+
+`tangoflux_config.yaml` defines the training file paths and model hyperparameters:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' src/train.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
+```
+
+To perform DPO training, modify the training files such that each data point contains "chosen", "reject", "caption" and "duration" fields. Please specify the path to your training files in `configs/tangoflux_config.yaml`. An example has been provided in `train_dpo.json`. Replace it with your own audio.
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' src/train_dpo.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml'
 ```
-Our evaluation shows that inferring with 50 steps yield the best results. A CFG scale of 3.5, 4, and 4.5 yield simliar quality output.
-For faster inference, consider setting steps to 25 that yield similar audio quality.
 
 ## Evaluation Scripts
 
@@ -77,15 +93,13 @@ This key comparison metrics include:
 
 All the inference times are observed on the same A40 GPU. The counts of trainable parameters are reported in the **\#Params** column.
 
-| Model                           | \#Params  | Duration | Steps | FD<sub>openl3</sub> ↓ | KL<sub>passt</sub> ↓ | CLAP<sub>score</sub> ↑ | IS ↑ | Inference Time (s) |
-|---------------------------------|-----------|----------|-------|-----------------------|----------------------|------------------------|------|--------------------|
-| **AudioLDM 2-large**            | 712M      | 10 sec   | 200   | 108.3                | 1.81                 | 0.419                  | 7.9  | 24.8               |
+| Model | Params | Duration | Steps | FD<sub>openl3</sub> ↓ | KL<sub>passt</sub> ↓ | CLAP<sub>score</sub> ↑ | IS ↑ | Inference Time (s) |
+|---|---|---|---|---|---|---|---|---|
+| **AudioLDM 2 (Large)** | 712M | 10 sec | 200 | 108.3 | 1.81 | 0.419 | 7.9 | 24.8 |
 | **Stable Audio Open** | 1056M | 47 sec | 100 | 89.2 | 2.58 | 0.291 | 9.9 | 8.6 |
-| **Tango 2**                     | 866M      | 10 sec   | 200   | 108.4                | **1.11**             | 0.447                  | 9.0  | 22.8               |
-| **TangoFlux-base**              | **515M**  | 30 sec   | 50    | 80.2                 | 1.22                 | 0.431                  | 11.7 | **3.7**            |
-| **TangoFlux**                   | **515M**  | 30 sec   | 50    | **75.1**             | 1.15                 | **0.480**              | **12.2** | **3.7**         |
-
-
+| **Tango 2** | 866M | 10 sec | 200 | 108.4 | 1.11 | 0.447 | 9.0 | 22.8 |
+| **TangoFlux (Base)** | 515M | 30 sec | 50 | 80.2 | 1.22 | 0.431 | 11.7 | 3.7 |
+| **TangoFlux** | 515M | 30 sec | 50 | 75.1 | 1.15 | 0.480 | 12.2 | 3.7 |
 
 ## Citation
 
@@ -100,3 +114,7 @@ All the inference times are observed on the same A40 GPU. The counts of trainabl
       url={https://arxiv.org/abs/2412.21037}, 
 }
 ```
+
+## License
+
+TangoFlux is licensed under the MIT License. See the `LICENSE` file for more details.
\ No newline at end of file

assets/image.txt

-

data/.gitkeep

+*.wav
\ No newline at end of file

inference.py

+import torchaudio
+from tangoflux import TangoFluxInference
+
+model = TangoFluxInference(name="declare-lab/TangoFlux")
+audio = model.generate("Hammer slowly hitting the wooden table", steps=50, duration=10)
+
+torchaudio.save("output.wav", audio, sample_rate=44100)

replicate_demo/predict.py

@@ -10,11 +10,13 @@ from diffusers import AutoencoderOobleck
 import soundfile as sf
 from safetensors.torch import load_file
 from huggingface_hub import snapshot_download
-from src.model import TangoFlux
+from tangoflux.model import TangoFlux
 from tangoflux import TangoFluxInference
 
 MODEL_CACHE = "model_cache"
-MODEL_URL = "https://weights.replicate.delivery/default/declare-lab/TangoFlux/model_cache.tar"
+MODEL_URL = (
+    "https://weights.replicate.delivery/default/declare-lab/TangoFlux/model_cache.tar"
+)
 
 
 class CachedTangoFluxInference(TangoFluxInference):

setup.py

+from setuptools import setup
+
+setup(
+    name="tangoflux",
+    description="TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching",
+    version="0.1.0",
+    packages=["tangoflux"],
+    install_requires=[
+        "torch==2.4.0",
+        "torchaudio==2.4.0",
+        "torchlibrosa==0.1.0",
+        "torchvision==0.19.0",
+        "transformers==4.44.0",
+        "diffusers==0.30.0",
+        "accelerate==0.34.2",
+        "datasets==2.21.0",
+        "librosa",
+        "tqdm",
+        "wandb",
+        "click",
+        "gradio",
+        "torchaudio",
+    ],
+    entry_points={
+        "console_scripts": [
+            "tangoflux=tangoflux.cli:main",
+            "tangoflux-demo=tangoflux.demo:main",
+        ],
+    },
+)

tangoflux.py → tangoflux/__init__.py

 from diffusers import AutoencoderOobleck
 import torch
-from transformers  import T5EncoderModel,T5TokenizerFast
+from transformers import T5EncoderModel, T5TokenizerFast
 from diffusers import FluxTransformer2DModel
 from torch import nn
 from typing import List
@@ -9,10 +9,10 @@ from diffusers.training_utils import compute_density_for_timestep_sampling
 import copy
 import torch.nn.functional as F
 import numpy as np
-from src.model import TangoFlux
+from tangoflux.model import TangoFlux
 from huggingface_hub import snapshot_download
 from tqdm import tqdm
-from typing import Optional,Union,List
+from typing import Optional, Union, List
 from datasets import load_dataset, Audio
 from math import pi
 import json
@@ -23,8 +23,11 @@ from safetensors.torch import load_file
 
 class TangoFluxInference:
 
-    def __init__(self,name='declare-lab/TangoFlux',device="cuda"):
-        
+    def __init__(
+        self,
+        name="declare-lab/TangoFlux",
+        device="cuda" if torch.cuda.is_available() else "cpu",
+    ):
 
         self.vae = AutoencoderOobleck()
 
@@ -33,29 +36,25 @@ class TangoFluxInference:
         self.vae.load_state_dict(vae_weights)
         weights = load_file("{}/tangoflux.safetensors".format(paths))
 
-        with open('{}/config.json'.format(paths),'r') as f:
+        with open("{}/config.json".format(paths), "r") as f:
             config = json.load(f)
         self.model = TangoFlux(config)
-        self.model.load_state_dict(weights,strict=False)
+        self.model.load_state_dict(weights, strict=False)
         # _IncompatibleKeys(missing_keys=['text_encoder.encoder.embed_tokens.weight'], unexpected_keys=[]) this behaviour is expected
         self.vae.to(device)
         self.model.to(device)
 
-    def generate(self,prompt,steps=25,duration=10,guidance_scale=4.5):
+    def generate(self, prompt, steps=25, duration=10, guidance_scale=4.5):
 
         with torch.no_grad():
-            latents = self.model.inference_flow(prompt,
+            latents = self.model.inference_flow(
+                prompt,
                 duration=duration,
                 num_inference_steps=steps,
-            guidance_scale=guidance_scale)
-
+                guidance_scale=guidance_scale,
+            )
 
-
-            wave = self.vae.decode(latents.transpose(2,1)).sample.cpu()[0]
+            wave = self.vae.decode(latents.transpose(2, 1)).sample.cpu()[0]
         waveform_end = int(duration * self.vae.config.sampling_rate)
         wave = wave[:, :waveform_end]
         return wave
-
-
-
-

tangoflux/cli.py

+import click
+import torchaudio
+from tangoflux import TangoFluxInference
+
+@click.command()
+@click.argument('prompt')
+@click.argument('output_file')
+@click.option('--duration', default=10, type=int, help='Duration in seconds (1-30)')
+@click.option('--steps', default=50, type=int, help='Number of inference steps (10-100)')
+def main(prompt: str, output_file: str, duration: int, steps: int):
+    """Generate audio from text using TangoFlux.
+    
+    Args:
+        prompt: Text description of the audio to generate
+        output_file: Path to save the generated audio file
+        duration: Duration of generated audio in seconds (default: 10)
+        steps: Number of inference steps (default: 50)
+    """
+    if not 1 <= duration <= 30:
+        raise click.BadParameter('Duration must be between 1 and 30 seconds')
+    if not 10 <= steps <= 100:
+        raise click.BadParameter('Steps must be between 10 and 100')
+
+    model = TangoFluxInference(name="declare-lab/TangoFlux")
+    audio = model.generate(prompt, steps=steps, duration=duration)
+    torchaudio.save(output_file, audio, sample_rate=44100)
+
+if __name__ == '__main__':
+    main()

tangoflux/demo.py

+import gradio as gr
+import torchaudio
+import click
+import tempfile
+from tangoflux import TangoFluxInference
+
+model = TangoFluxInference(name="declare-lab/TangoFlux")
+
+
+def generate_audio(prompt, duration, steps):
+    audio = model.generate(prompt, steps=steps, duration=duration)
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
+        torchaudio.save(f.name, audio, sample_rate=44100)
+        return f.name
+
+
+examples = [
+    ["Hammer slowly hitting the wooden table", 10, 50],
+    ["Gentle rain falling on a tin roof", 15, 50],
+    ["Wind chimes tinkling in a light breeze", 10, 50],
+    ["Rhythmic wooden table tapping overlaid with steady water pouring sound", 10, 50],
+]
+
+with gr.Blocks(title="TangoFlux Text-to-Audio Generation") as demo:
+    gr.Markdown("# TangoFlux Text-to-Audio Generation")
+    gr.Markdown("Generate audio from text descriptions using TangoFlux")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Text Prompt", placeholder="Enter your audio description..."
+            )
+            duration = gr.Slider(
+                minimum=1, maximum=30, value=10, step=1, label="Duration (seconds)"
+            )
+            steps = gr.Slider(
+                minimum=10, maximum=100, value=50, step=10, label="Number of Steps"
+            )
+            generate_btn = gr.Button("Generate Audio")
+
+        with gr.Column():
+            audio_output = gr.Audio(label="Generated Audio")
+
+    generate_btn.click(
+        fn=generate_audio, inputs=[prompt, duration, steps], outputs=audio_output
+    )
+
+    gr.Examples(
+        examples=examples,
+        inputs=[prompt, duration, steps],
+        outputs=audio_output,
+        fn=generate_audio,
+    )
+
+@click.command()
+@click.option('--host', default='127.0.0.1', help='Host to bind to')
+@click.option('--port', default=None, help='Port to bind to')
+@click.option('--share', is_flag=True, help='Enable sharing via Gradio')
+def main(host, port, share):
+    demo.queue().launch(server_name=host, server_port=port, share=share)
+
+if __name__ == "__main__":
+    main()

src/utils.py → tangoflux/utils.py

@@ -31,8 +31,6 @@ def pad_wav(waveform, segment_length):
         return waveform
 
 
-
-
 def read_wav_file(filename, duration_sec):
     info = torchaudio.info(filename)
     sample_rate = info.sample_rate
@@ -40,29 +38,38 @@ def read_wav_file(filename, duration_sec):
     # Calculate the number of frames corresponding to the desired duration
     num_frames = int(sample_rate * duration_sec)
 
-    waveform, sr = torchaudio.load(filename,num_frames=num_frames)  # Faster!!!
+    waveform, sr = torchaudio.load(filename, num_frames=num_frames)  # Faster!!!
 
-
-    if waveform.shape[0] == 2 : ## Stereo audio
+    if waveform.shape[0] == 2:  ## Stereo audio
         resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=44100)
         resampled_waveform = resampler(waveform)
-        #print(resampled_waveform.shape)
-        padded_left = pad_wav(resampled_waveform[0], int(44100*duration_sec)) ## We pad left and right seperately
-        padded_right = pad_wav(resampled_waveform[1], int(44100*duration_sec))
+        # print(resampled_waveform.shape)
+        padded_left = pad_wav(
+            resampled_waveform[0], int(44100 * duration_sec)
+        )  ## We pad left and right seperately
+        padded_right = pad_wav(resampled_waveform[1], int(44100 * duration_sec))
 
-        return torch.stack([padded_left,padded_right])
+        return torch.stack([padded_left, padded_right])
     else:
-        waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=44100)[0]
-        waveform = pad_wav(waveform, int(44100*duration_sec)).unsqueeze(0)
+        waveform = torchaudio.functional.resample(
+            waveform, orig_freq=sr, new_freq=44100
+        )[0]
+        waveform = pad_wav(waveform, int(44100 * duration_sec)).unsqueeze(0)
 
         return waveform
 
 
-
-
-
 class DPOText2AudioDataset(Dataset):
-    def __init__(self, dataset, prefix, text_column, audio_w_column, audio_l_column, duration, num_examples=-1):
+    def __init__(
+        self,
+        dataset,
+        prefix,
+        text_column,
+        audio_w_column,
+        audio_l_column,
+        duration,
+        num_examples=-1,
+    ):
 
         inputs = list(dataset[text_column])
         self.inputs = [prefix + inp for inp in inputs]
@@ -72,11 +79,18 @@ class DPOText2AudioDataset(Dataset):
         self.indices = list(range(len(self.inputs)))
 
         self.mapper = {}
-        for index, audio_w, audio_l, duration, text in zip(self.indices, self.audios_w,self.audios_l,self.durations,inputs):
+        for index, audio_w, audio_l, duration, text in zip(
+            self.indices, self.audios_w, self.audios_l, self.durations, inputs
+        ):
             self.mapper[index] = [audio_w, audio_l, duration, text]
 
         if num_examples != -1:
-            self.inputs, self.audios_w, self.audios_l, self.durations = self.inputs[:num_examples], self.audios_w[:num_examples], self.audios_l[:num_examples],  self.durations[:num_examples]
+            self.inputs, self.audios_w, self.audios_l, self.durations = (
+                self.inputs[:num_examples],
+                self.audios_w[:num_examples],
+                self.audios_l[:num_examples],
+                self.durations[:num_examples],
+            )
             self.indices = self.indices[:num_examples]
 
     def __len__(self):
@@ -86,15 +100,24 @@ class DPOText2AudioDataset(Dataset):
         return len(self.inputs)
 
     def __getitem__(self, index):
-        s1, s2, s3, s4, s5 = self.inputs[index], self.audios_w[index], self.audios_l[index], self.durations[index], self.indices[index]
+        s1, s2, s3, s4, s5 = (
+            self.inputs[index],
+            self.audios_w[index],
+            self.audios_l[index],
+            self.durations[index],
+            self.indices[index],
+        )
         return s1, s2, s3, s4, s5
 
     def collate_fn(self, data):
         dat = pd.DataFrame(data)
         return [dat[i].tolist() for i in dat]
 
+
 class Text2AudioDataset(Dataset):
-    def __init__(self, dataset, prefix, text_column, audio_column, duration, num_examples=-1):
+    def __init__(
+        self, dataset, prefix, text_column, audio_column, duration, num_examples=-1
+    ):
 
         inputs = list(dataset[text_column])
         self.inputs = [prefix + inp for inp in inputs]
@@ -103,11 +126,17 @@ class Text2AudioDataset(Dataset):
         self.indices = list(range(len(self.inputs)))
 
         self.mapper = {}
-        for index, audio, duration,text in zip(self.indices, self.audios, self.durations,inputs):
-            self.mapper[index] = [audio, text,duration]
+        for index, audio, duration, text in zip(
+            self.indices, self.audios, self.durations, inputs
+        ):
+            self.mapper[index] = [audio, text, duration]
 
         if num_examples != -1:
-            self.inputs, self.audios, self.durations = self.inputs[:num_examples], self.audios[:num_examples], self.durations[:num_examples]
+            self.inputs, self.audios, self.durations = (
+                self.inputs[:num_examples],
+                self.audios[:num_examples],
+                self.durations[:num_examples],
+            )
             self.indices = self.indices[:num_examples]
 
     def __len__(self):
@@ -117,7 +146,12 @@ class Text2AudioDataset(Dataset):
         return len(self.inputs)
 
     def __getitem__(self, index):
-        s1, s2, s3, s4 = self.inputs[index], self.audios[index], self.durations[index], self.indices[index]
+        s1, s2, s3, s4 = (
+            self.inputs[index],
+            self.audios[index],
+            self.durations[index],
+            self.indices[index],
+        )
         return s1, s2, s3, s4
 
     def collate_fn(self, data):

train.sh

 
-CUDA_VISISBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' src/train.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml' 
\ No newline at end of file
+CUDA_VISISBLE_DEVICES=0,1 accelerate launch --config_file='configs/accelerator_config.yaml' tangoflux/train.py   --checkpointing_steps="best" --save_every=5 --config='configs/tangoflux_config.yaml' 
\ No newline at end of file