richardjiang736/musicgen_lyre_0_new 🔢📝🖼️✓❓ → 🖼️
About
Generate Ancient Greek Lyre Instrumentals
Example Output
Prompt:
"ancient, instrumental, acoustic, lyre, light, reflective, peaceful"
Output
Performance Metrics
244.94s
Prediction Time
329.38s
Total Time
All Input Parameters
{
"top_k": 250,
"top_p": 0,
"prompt": "ancient, instrumental, acoustic, lyre, light, reflective, peaceful",
"duration": 15,
"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/yhqm/8jw3sZ0kK8ozOxf6EAJqxPDKuUQfqVehMVmogpctKgqnS6OqA/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 2801412088
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CLIPPING out happening with proba (a bit of clipping is okay): 0.00013749999925494194 maximum scale: 1.2546577453613281
Version Details
- Version ID
c9d5b3c81d3485241c909bdbfa21ffbade001f14c4ede8fce55a5d7b98680976- Version Created
- August 4, 2025