Instructions to use awkyu/audiogen-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use awkyu/audiogen-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="awkyu/audiogen-medium")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("awkyu/audiogen-medium") model = AutoModelForTextToWaveform.from_pretrained("awkyu/audiogen-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| # AudioGen - Medium - 1.5B | |
| AudioGen is an autoregressive transformer LM that synthesizes general audio conditioned on text (Text-to-Audio). | |
| Internally, AudioGen operates over discrete representations learnt from the raw waveform, using an EnCodec tokenizer. | |
| AudioGen was presented at [AudioGen: Textually Guided Audio Generation](https://arxiv.org/abs/2209.15352) by *Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, Yossi Adi*. | |
| AudioGen 1.5B is a variant of the original AudioGen model that follows [MusicGen](https://arxiv.org/abs/2306.05284) architecture. | |
| More specifically, it is trained over a 16kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz with a delay pattern between the codebooks. | |
| Having only 50 auto-regressive steps per second of audio, this AudioGen model allows faster generation while reaching similar performances to the original AudioGen model introduced in the paper. | |
| ## Audiocraft Usage | |
| You can run AudioGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): | |
| 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) | |
| ``` | |
| pip install git+https://github.com/facebookresearch/audiocraft.git | |
| ``` | |
| 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: | |
| ``` | |
| apt get install ffmpeg | |
| ``` | |
| 3. Run the following Python code: | |
| ```py | |
| import torchaudio | |
| from audiocraft.models import AudioGen | |
| from audiocraft.data.audio import audio_write | |
| model = AudioGen.get_pretrained('facebook/audiogen-medium') | |
| model.set_generation_params(duration=5) # generate 5 seconds. | |
| descriptions = ['dog barking', 'sirenes of an emergency vehicule', 'footsteps in a corridor'] | |
| wav = model.generate(descriptions) # generates 3 samples. | |
| for idx, one_wav in enumerate(wav): | |
| # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. | |
| audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True) | |
| ``` | |
| ## Model details | |
| See [AudioGen's model card](https://github.com/facebookresearch/audiocraft/blob/main/model_cards/AUDIOGEN_MODEL_CARD.md). |