fastai code music art text-generation-inference merge moe legal chemistry climate finance text-to-audio en dataset:nvidia/OpenMathInstruct-1 dataset:HuggingFaceTB/cosmopedia arxiv:1910.09700 license:apache-2.0 region:us
---
license: apache-2.0
datasets:
- nvidia/OpenMathInstruct-1
- HuggingFaceTB/cosmopedia
language:
- en
metrics:
- accuracy
library_name: fastai
pipeline_tag: text-to-audio
tags:
- code
- music
- art
- text-generation-inference
- merge
- moe
- legal
- chemistry
- climate
- finance
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->The "Soul Train" ebonix model is Implemented using the fastai library, it converts text to audio,
making it suitable for various applications such as music, art, legal, and scientific domains.

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details "SoulTrain" 
model represents an innovative application of NLP technology
tailored to a specific cultural and linguistic context, with potential applications spanning a wide range of fields and industries.

### Model Description Soul Train"
model into their research, teaching, and advocacy efforts, highlighting its potential impact on linguistic studies and cultural awareness of branches connect2universe

<!-- Provide a longer summary of what this model is. --> The "Soul Train" 
model is a text-to-audio system trained to generate speech in Ebonix, a form of American English
associated with African American culture. It's licensed under Apache-2.0 and utilizes datasets like
nvidia/OpenMathInstruct-1 and HuggingFaceTB/cosmopedia. The model supports English language processing
and focuses on accuracy. Implemented using the fastai library, it converts text to audio,
making it suitable for various applications such as music, art, legal, and scientific domains.



- **Developed by:** Jason C Smith A Blackmale [XSH.ONE XSH-Hero]
- **Funded by [BlackUnicornFactory]:** [BUF]
- **Shared by [Extended_Sound-Hero]:** [grabbytabby-shx.one]
- **Model type:** [SOULTRAIN]
- **Language(s) (NLP):** [Ebonix, a variety of American English commonly spoken by African Americans]
- **License:** [Apache-2.0 license.]
- **Finetuned from model [SoulTrain]:** [By fine-tuning the "Soul Train" model using RAG,
-  we can enhance its ability to generate contextually relevant and culturally appropriate responses in Ebonix.
-   The incorporation of a retriever component ensures that the generated outputs are grounded in relevant knowledge,
-    leading to more informative and engaging interactions.





 ]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->https://github.com/grabbytabby/SHX.ONE-BLOCKCHAIN-Mminer

- **Repository:** [https://github.com/grabbytabby/soultrain]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This rendition captures the essence of the topic using structured language and thematic coherence typical of responses generated by large language models
### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[from transformers import Trainer, TrainingArguments, GPT2Tokenizer, GPT2LMHeadModel

# Load pre-trained model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Define training text as a prompt
training_text = """
The "Soul Train" model, engineered to facilitate Ebonix speech generation, presents a comprehensive utility spectrum, engaging diverse stakeholders and societal discourse. Examination of its intended usage and consequential effects illuminates its dynamic significance across linguistic, cultural, and educational realms.

Principal users of the "Soul Train" model encompass a heterogeneous cohort. Linguistic scholars, immersed in language variation and sociocultural dynamics, anticipate leveraging its capabilities to dissect Ebonix intricacies, enriching sociolinguistic discourse and cultural anthropology. Simultaneously, educators, tasked with cultural and linguistic diversity pedagogy, may embed the model within curricula, nurturing cultural awareness and linguistic pluralism among students.

Beyond academia, creatives across literature, music, and film domains seek to harness the model's prowess to authentically portray African American cultural tenets through nuanced linguistic representation. Additionally, social media influencers, attuned to the model's resonance with African American audiences, aim to deploy it for culturally resonant content creation, enhancing digital engagement strategies.

Concurrently, the model's impact transcends mere utility, affecting various societal segments. Within the African American community, its utilization fosters cultural reclamation and linguistic pride, challenging derogatory stereotypes associated with nonstandard dialects. However, it also implicates language users and learners, whose perceptions of linguistic norms may be shaped by the model's adoption, necessitating discourse on linguistic integrity and appropriation.

Moreover, its availability sparks broader societal dialogue on linguistic diversity, cultural representation, and inclusivity, necessitating ethical scrutiny and stakeholder engagement. Developers, researchers, and users are urged to navigate the ethical landscape, mindful of cultural appropriation, linguistic integrity, and equitable representation imperatives.

In essence, the "Soul Train" model embodies linguistic innovation, cultural celebration, and ethical reflection, emblematic of technology's interaction with societal evolution. Its judicious application, guided by ethical considerations and stakeholder engagement, is vital in navigating linguistic diversity and societal harmony.
"""

# Tokenize the training text
inputs = tokenizer(training_text, return_tensors="pt")

# Define training arguments
training_args = TrainingArguments(
    output_dir="./soul_train_training",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=2,
    save_steps=10_000,
    save_total_limit=2,
    prediction_loss_only=True,
)

# Define Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=inputs,
)

# Train the model
trainer.train()


### Downstream Use [SOULTRAIN]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load fine-tuned "Soul Train" model and tokenizer
model_name = "./soul_train_fine_tuned"
tokenizer = GPT2Tokenizer.from_pretrained(SHX_SoulTrain)
model = GPT2LMHeadModel.from_pretrained(XSH_SoulTrain)

# Define a prompt for generating Ebonix speech
prompt = "What's up, fam? Let's chill and vibe."

# Tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")

# Generate Ebonix speech
output = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.8)

# Decode and print the generated speech
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Ebonix speech:", generated_text)]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[Misuse:

Cultural Appropriation: The "Soul Train" model should not be used to appropriate or caricature African American culture.
Care should be taken to respect the cultural significance of Ebonix and avoid reinforcing stereotypes or misrepresentations.
Propagation of Harmful Content: Users should refrain from using the model to generate speech that promotes hate speech, 
violence, or discrimination against any group or individual.
Malicious Use:

Dissemination of Misinformation: Malicious actors could exploit the model to generate false or misleading information, potentially leading to misinformation
campaigns or the spread of rumors.Manipulation and Deception: The model could be misused to impersonate individuals or organizations, deceive people, or create fraudulent
content.

Limitations:
Contextual Understanding: The "Soul Train" model may struggle with understanding context, sarcasm, or nuanced meanings, leading to inaccurate or inappropriate responses in certain situations.
Biases in Training Data: If the model is trained on biased or unrepresentative datasets, it may perpetuate or amplify existing biases in its generated output, potentially reinforcing stereotypes or marginalizing certain groups.
Accuracy and Coherence: While the model excels at generating Ebonix speech, its output may still exhibit occasional inaccuracies, inconsistencies, or lack of coherence, especially with complex or nuanced prompts. ]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[Addressing both technical and sociotechnical limitations of the "Soul Train" model is crucial for understanding its capabilities and potential challenges in real-world applications. Here's an overview of these limitations:

1. **Technical Limitations**:
   - **Data Bias**: The "Soul Train" model's performance may be influenced by biases present in the training data. If the training data is not diverse or representative enough, the model may struggle to accurately capture the full spectrum of linguistic variations and cultural nuances present in Ebonix.
   - **Context Sensitivity**: The model's understanding of context may be limited, leading to occasional inaccuracies or misunderstandings, especially in situations requiring nuanced interpretation or cultural sensitivity.
   - **Scalability**: Generating Ebonix speech with high accuracy and coherence may require significant computational resources and time, limiting the model's scalability for large-scale applications or real-time interactions.
   - **Fine-tuning Requirements**: Fine-tuning the model for specific tasks or domains may require substantial labeled data and expertise, making it challenging to adapt the model to niche or specialized applications.

2. **Sociotechnical Limitations**:
   - **Ethical Considerations**: The deployment of the "Soul Train" model raises ethical questions regarding cultural appropriation, representation, and potential reinforcement of stereotypes. Careful consideration is needed to ensure that the model's use respects cultural sensitivities and promotes inclusivity.
   - **User Expectations**: Users interacting with the model may have varying expectations regarding its capabilities and limitations. Managing user expectations and providing clear guidance on the model's capabilities can help mitigate frustration and disappointment.
   - **Impact on Language Evolution**: The widespread adoption of the model could influence the evolution of Ebonix and other dialects, potentially shaping linguistic norms and usage patterns over time. Understanding and monitoring these sociolinguistic dynamics is essential to assess the model's long-term impact accurately.

Addressing these technical and sociotechnical limitations requires a multidisciplinary approach that encompasses expertise in natural language processing, sociolinguistics, ethics, and cultural studies. Strategies for mitigating these limitations include:
   
- **Continuous Evaluation**: Regularly assessing the model's performance, biases, and impact on users and communities to identify areas for improvement and potential risks.
- **Transparency and Accountability**: Providing transparent documentation of the model's development process, training data, and limitations to foster trust and accountability among users and stakeholders.
- **Community Engagement**: Engaging with affected communities, linguistic experts, and diverse stakeholders to solicit feedback, address concerns, and ensure that the model's deployment aligns with community values and needs.
- **Algorithmic Fairness**: Implementing fairness-aware techniques to mitigate biases and ensure equitable outcomes, particularly for marginalized or underrepresented groups.

By acknowledging and addressing these technical and sociotechnical limitations, developers and practitioners can strive to maximize the positive impact of the "Soul Train" model while minimizing potential risks and unintended consequences. ]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]

                                      (Mm.Module):
        def _init_(self, in_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
		       super()._init_() 
			   filter_channels =in_channels #it needs to be removed from xsh.one version 
			   self.in_channels = in_channels 
			   self.filter_channels =filter_channels
			   self.kernel_size =_kernel_size
			   self.p_dropout = p_dropout
			   self.n_flows = n_flows
			   self.gin_channels = gin_channels
			   
			   self.one = xsh.one