Create README.md

README.md

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+---
+license: cc-by-nc-nd-4.0
+---
+
+# AudioLDM 2 Large
+
+AudioLDM 2 is a latent text-to-audio diffusion model capable of generating realistic audio samples given any text input. 
+It is available in the 🧨 Diffusers library from v0.21.0 onwards.
+
+# Model Details
+
+AudioLDM 2 was proposed in the paper [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://arxiv.org/abs/2308.05734) by Haohe Liu et al.
+
+AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, 
+human speech and music.
+
+# Checkpoint Details
+
+This is the original, **large** version of the AudioLDM 2 model, also referred to as **audioldm2-full-large-1150k**. 
+
+There are three official AudioLDM 2 checkpoints. Two of these checkpoints are applicable to the general task of text-to-audio 
+generation. The third checkpoint is trained exclusively on text-to-music generation. All checkpoints share the same 
+model size for the text encoders and VAE. They differ in the size and depth of the UNet. See table below for details on 
+the three official checkpoints:
+
+| Checkpoint                                                      | Task          | UNet Model Size | Total Model Size | Training Data / h |
+|-----------------------------------------------------------------|---------------|-----------------|------------------|-------------------|
+| [audioldm2](https://huggingface.co/cvssp/audioldm2)             | Text-to-audio | 350M            | 1.1B             | 1150k             |
+| [audioldm2-large](https://huggingface.co/cvssp/audioldm2-large) | Text-to-audio | 750M            | 1.5B             | 1150k             |
+| [audioldm2-music](https://huggingface.co/cvssp/audioldm2-music) | Text-to-music | 350M            | 1.1B             | 665k              |
+
+## Model Sources
+
+- [**Original Repository**](https://github.com/haoheliu/audioldm2)
+- [**🧨 Diffusers Pipeline**](https://huggingface.co/docs/diffusers/api/pipelines/audioldm2)
+- [**Paper**](https://arxiv.org/abs/2308.05734)
+- [**Demo**](https://huggingface.co/spaces/haoheliu/audioldm2-text2audio-text2music)
+
+# Usage
+
+First, install the required packages:
+
+```
+pip install --upgrade diffusers transformers
+```
+
+## Text-to-Audio
+
+For text-to-audio generation, the [AudioLDM2Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/audioldm2) can be 
+used to load pre-trained weights and generate text-conditional audio outputs:
+
+```python
+from diffusers import AudioLDM2Pipeline
+import torch
+
+repo_id = "cvssp/audioldm2-large"
+pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "The sound of a hammer hitting a wooden surface"
+audio = pipe(prompt, num_inference_steps=200, audio_length_in_s=10.0).audios[0]
+```
+
+The resulting audio output can be saved as a .wav file:
+```python
+import scipy
+
+scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
+```
+
+Or displayed in a Jupyter Notebook / Google Colab:
+```python
+from IPython.display import Audio
+
+Audio(audio, rate=16000)
+```
+
+## Tips
+
+Prompts:
+* Descriptive prompt inputs work best: you can use adjectives to describe the sound (e.g. "high quality" or "clear") and make the prompt context specific (e.g., "water stream in a forest" instead of "stream").
+* It's best to use general terms like 'cat' or 'dog' instead of specific names or abstract objects that the model may not be familiar with.
+
+Inference:
+* The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument: higher steps give higher quality audio at the expense of slower inference.
+* The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument.
+
+When evaluating generated waveforms:
+
+* The quality of the generated waveforms can vary significantly based on the seed. Try generating with different seeds until you find a satisfactory generation
+* Multiple waveforms can be generated in one go: set `num_waveforms_per_prompt` to a value greater than 1. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.
+
+The following example demonstrates how to construct a good audio generation using the aforementioned tips:
+
+```python
+import scipy
+import torch
+from diffusers import AudioLDM2Pipeline
+
+# load the pipeline
+repo_id = "cvssp/audioldm2-large"
+pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+# define the prompts
+prompt = "The sound of a hammer hitting a wooden surface"
+negative_prompt = "Low quality."
+
+# set the seed
+generator = torch.Generator("cuda").manual_seed(0)
+
+# run the generation
+audio = pipe(
+    prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=200,
+    audio_length_in_s=10.0,
+    num_waveforms_per_prompt=3,
+).audios
+
+# save the best audio sample (index 0) as a .wav file
+scipy.io.wavfile.write("techno.wav", rate=16000, data=audio[0])
+```
+
+# Citation
+
+**BibTeX:**
+```
+@article{liu2023audioldm2,
+  title={"AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining"},
+  author={Haohe Liu and Qiao Tian and Yi Yuan and Xubo Liu and Xinhao Mei and Qiuqiang Kong and Yuping Wang and Wenwu Wang and Yuxuan Wang and Mark D. Plumbley},
+  journal={arXiv preprint arXiv:2308.05734},
+  year={2023}
+}
+```