datasci888/musicgen 🔢📝🖼️✓❓ → 🖼️
About
Example Output
Prompt:
"produce a song melody in jog raga indian style"
Output
Performance Metrics
80.36s
Prediction Time
514.60s
Total Time
All Input Parameters
{
"top_k": 250,
"top_p": 0,
"prompt": "produce a song melody in jog raga indian style",
"duration": 60,
"temperature": 0.9,
"continuation": false,
"output_format": "wav",
"continuation_end": 0,
"continuation_start": 0,
"multi_band_diffusion": false,
"normalization_strategy": "rms",
"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/nfGAucPYkd28GCQvKNFd6mNc9F0Kz71m3CSoMchc2PAhxkTJA/trained_model.tar" to /src/models/hub/checkpoints/trained_model.tar 0%| | 0.00/1.57G [00:00<?, ?B/s] 0%| | 2.52M/1.57G [00:00<01:03, 26.4MB/s] 0%| | 5.16M/1.57G [00:00<01:01, 27.0MB/s] 0%| | 7.77M/1.57G [00:00<01:01, 27.1MB/s] 1%| | 10.7M/1.57G [00:00<00:58, 28.4MB/s] 1%| | 13.4M/1.57G [00:00<01:01, 27.3MB/s] 1%| | 16.0M/1.57G [00:00<01:01, 27.2MB/s] 1%| | 18.6M/1.57G [00:00<01:02, 26.6MB/s] 1%|▏ | 21.2M/1.57G [00:00<01:02, 26.5MB/s] 1%|▏ | 23.7M/1.57G [00:00<01:02, 26.4MB/s] 2%|▏ | 26.2M/1.57G [00:01<01:02, 26.4MB/s] 2%|▏ | 29.0M/1.57G [00:01<01:00, 27.1MB/s] 2%|▏ | 31.6M/1.57G [00:01<01:03, 25.9MB/s] 2%|▏ | 34.1M/1.57G [00:01<01:04, 25.4MB/s] 2%|▏ | 36.6M/1.57G [00:01<01:03, 25.7MB/s] 2%|▏ | 39.1M/1.57G [00:01<01:05, 25.0MB/s] 3%|▎ | 41.6M/1.57G [00:01<01:05, 25.2MB/s] 3%|▎ | 44.2M/1.57G [00:01<01:03, 25.9MB/s] 3%|▎ | 46.8M/1.57G [00:01<01:02, 26.2MB/s] 3%|▎ | 49.3M/1.57G [00:01<01:03, 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1.55G/1.57G [00:11<00:00, 475MB/s] 100%|██████████| 1.57G/1.57G [00:11<00:00, 149MB/s] Downloading config.json: 0%| | 0.00/758 [00:00<?, ?B/s] Downloading config.json: 100%|██████████| 758/758 [00:00<00:00, 834kB/s] Downloading model.safetensors: 0%| | 0.00/236M [00:00<?, ?B/s] Downloading model.safetensors: 18%|█▊ | 41.9M/236M [00:00<00:00, 388MB/s] Downloading model.safetensors: 36%|███▌ | 83.9M/236M [00:00<00:00, 377MB/s] Downloading model.safetensors: 58%|█████▊ | 136M/236M [00:00<00:00, 435MB/s] Downloading model.safetensors: 80%|███████▉ | 189M/236M [00:00<00:00, 448MB/s] Downloading model.safetensors: 100%|██████████| 236M/236M [00:00<00:00, 444MB/s] Using seed 2707079814 Step 1/3 1 / 1500 2 / 1500 3 / 1500 4 / 1500 5 / 1500 6 / 1500 7 / 1500 8 / 1500 9 / 1500 10 / 1500 11 / 1500 12 / 1500 13 / 1500 14 / 1500 15 / 1500 16 / 1500 17 / 1500 18 / 1500 19 / 1500 20 / 1500 21 / 1500 22 / 1500 23 / 1500 24 / 1500 25 / 1500 26 / 1500 27 / 1500 28 / 1500 29 / 1500 30 / 1500 31 / 1500 32 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Version Details
- Version ID
4766cb4985d3c6232b2b89bd681740aa1e34c0730f0df9c3a0554f25988ff9a5- Version Created
- April 4, 2024