ArtooDtoo/Orpheus_PTBR_FT_Unsloth_Chk-600

Model Card for Model ID

Unsloth's Orpheus Fine-Tune on freds0/BRSpeech-Leni Dataset

Model Details

Model Description

These are adapter (LoRA) files resulting from a fine-tune process of Canopy Lab's Orpheus 3b TTS es/it FT model on a Brazilian Portuguese dataset (freds0/BRSpeech-Leni) using Unsloth's notebook.

They may be used for inference or continued training, although results are still not yielding intended accuracy/naturality.

Recommendations are to either use a third-party inference provider for inexpensive testing (eg. HuggingFace itself), or download and continue training following Unsloth's scripts and instructions.

• Developed by: J. Arthur Quintieri De Toledo (ArtooDtoo) - fine-tuning and adapting
• Funded by [optional]: Co-Human
• Shared by [optional]: [More Information Needed]
• Model type: TTS
• Language(s) (NLP): Brazilian Portuguese
• License: [More Information Needed]
• Finetuned from model [optional]: Orpheus 3b Es/It Fine-Tuned (canopylabs/3b-es_it-ft-research_release)

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Unsloth's notebook, available at https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

• PEFT 0.15.2