Instructions to use itriedcoding/Sol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use itriedcoding/Sol with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="itriedcoding/Sol")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("itriedcoding/Sol") model = AutoModelForTextToSpectrogram.from_pretrained("itriedcoding/Sol", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add fine-tuned Sol weights and tokenizer
Browse files- generation_config.json +1 -1
- model.safetensors +1 -1
- sol_meta.json +4 -2
- speaker_embedding.npy +1 -1
generation_config.json
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@@ -9,5 +9,5 @@
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"output_hidden_states": false,
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"pad_token_id": 1,
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"transformers_version": "5.10.2",
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"use_cache":
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}
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"output_hidden_states": false,
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"pad_token_id": 1,
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"transformers_version": "5.10.2",
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"use_cache": true
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}
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model.safetensors
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 577789320
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version https://git-lfs.github.com/spec/v1
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oid sha256:20f16961a19032a926320e405a45f7d893c700e145f93f477a7cea7ad2bd213b
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size 577789320
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sol_meta.json
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@@ -4,6 +4,8 @@
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"dataset": "openslr/librispeech_asr/clean/validation",
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"speaker_id": 2277,
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"num_samples": 87,
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"max_steps":
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"sampling_rate": 16000
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}
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"dataset": "openslr/librispeech_asr/clean/validation",
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"speaker_id": 2277,
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"num_samples": 87,
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"max_steps": 80,
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"sampling_rate": 16000,
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"learning_rate": 2e-06,
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"note": "light fine-tune; speaker embedding is a raw x-vector (norm ~320), do not unit-normalise"
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}
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speaker_embedding.npy
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 2176
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| 1 |
version https://git-lfs.github.com/spec/v1
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+
oid sha256:55e29186e2af3db9014ca1b3fc7c51a14de1d36448fec1d63181f39b12336de1
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size 2176
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