sakemin/musicgen-bbongjjak 🔢📝🖼️✓❓ → 🖼️
About
MusicGen stereo-medium model fine-tuned on Bbongjjak(뽕짝), the Korean modern electronic pop music genre, with the text token 'Bbongjjak'.
Example Output
Prompt:
"Bbongjjak"
Output
Performance Metrics
178.74s
Prediction Time
287.33s
Total Time
All Input Parameters
{
"top_k": 250,
"top_p": 0,
"prompt": "Bbongjjak",
"duration": 30,
"temperature": 1,
"continuation": false,
"output_format": "wav",
"continuation_start": 0,
"multi_band_diffusion": false,
"normalization_strategy": "loudness",
"classifier_free_guidance": 3
}
Input Parameters
- seed
- Seed for random number generator. If None or -1, a random seed will be used.
- top_k
- Reduces sampling to the k most likely tokens.
- top_p
- Reduces sampling to tokens with cumulative probability of p. When set to `0` (default), top_k sampling is used.
- prompt
- A description of the music you want to generate.
- duration
- Duration of the generated audio in seconds.
- input_audio
- An audio file that will influence the generated music. If `continuation` is `True`, the generated music will be a continuation of the audio file. Otherwise, the generated music will mimic the audio file's melody.
- temperature
- Controls the 'conservativeness' of the sampling process. Higher temperature means more diversity.
- continuation
- If `True`, generated music will continue `melody`. Otherwise, generated music will mimic `audio_input`'s melody.
- output_format
- Output format for generated audio.
- continuation_end
- End time of the audio file to use for continuation. If -1 or None, will default to the end of the audio clip.
- replicate_weights
- Replicate MusicGen weights to use. Leave blank to use the default weights.
- continuation_start
- Start time of the audio file to use for continuation.
- multi_band_diffusion
- If `True`, the EnCodec tokens will be decoded with MultiBand Diffusion. Only works with non-stereo models.
- normalization_strategy
- Strategy for normalizing audio.
- classifier_free_guidance
- Increases the influence of inputs on the output. Higher values produce lower-varience outputs that adhere more closely to inputs.
Output Schema
Output
Example Execution Logs
Downloading: "https://replicate.delivery/pbxt/s32yD6AM475jGtJy401Pdhb9XM11YYVeqpecwNdnIPwiksYSA/trained_model.tar" to /src/models/hub/checkpoints/trained_model.tar
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/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
Fine-tuned model weights hot-swapped!
Using seed 2119450570
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CLIPPING out happening with proba (a bit of clipping is okay): 5.208333391237829e-07 maximum scale: 1.0151841640472412
Version Details
- Version ID
2ceacf29e48dd54757e12d9dc8e9b6da697f691221191ace6df0811aedfa1f1e- Version Created
- February 20, 2024