Create README.md

README.md

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+---
+license: mit
+tags:
+- audiocraft
+base_model: facebook/musicgen-small
+---
+
+
+small model trained on 295 freely available drum breaks. No text conditioning was used (inspired by https://github.com/aaronabebe/micro-musicgen). 
+
+only trained for 5 epochs, liked the sound there but can resume training with continue_from=checkpoint.th
+
+
+useful docs: https://github.com/facebookresearch/audiocraft/blob/main/docs/TRAINING.md
+
+
+```
+dora run solver=musicgen/musicgen_base_32khz model/lm/model_scale=small conditioner=none dataset.batch_size=5 dset=audio/breaks.yaml dataset.valid.num_samples=1 generate.every=10000 evaluate.every=10000 optim.optimizer=adamw optim.lr=1e-4 optim.adam.weight_decay=0.01 checkpoint.save_every=5
+```
+
+
+use: 
+
+```
+import torchaudio
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
+model = MusicGen.get_pretrained('mateo-19182/all-the-breaks')
+model.set_generation_params(duration=10)
+wav = model.generate_unconditional(10)
+
+for idx, one_wav in enumerate(wav):
+    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
+```