atlithor
/

RepeaTTS-level-3

@@ -1,176 +1,49 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **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. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### 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:**
@@ -178,22 +51,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 **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]

 ---
 library_name: transformers
+license: cc
+datasets:
+- atlithor/talromur3_without_emotions
+language:
+- is
+base_model:
+- parler-tts/parler-tts-mini-multilingual-v1.1
+pipeline_tag: text-to-speech
 ---
+# Model Card for RepeaTTS-level-3
+See [Emotive Icelandic](https://huggingface.co/atlithor/EmotiveIcelandic) for more information about this model and the data that it is trained on.
+The RepeaTTS series is trained on the same data as Emotive Icelandic, but without emotive content disclosure.
+This model, level-3, corresponds to a model with a double-refined subset of the original training corpus. The model can be, additionally, prompted
+with a "neutral" label, or an intensity label:
+- low intensity: voice is low expressive
+- high intensity: voice is very expressive
+## Usage
 Use the code below to get started with the model.
+```py
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = ParlerTTSForConditionalGeneration.from_pretrained("atlithor/RepeaTTS-level-3").to(device)
+tokenizer = AutoTokenizer.from_pretrained("atlithor/EmotiveIcelandic")
+description_tokenizer = AutoTokenizer.from_pretrained(model.config.text_encoder._name_or_path)
+prompt = "Þetta er frábær hugmynd!" # E: this is a great idea!
+description = "The recording is of very high quality, with Ingrid's voice sounding clear and very close up. Ingrid speaks at very high intensity."
+input_ids = description_tokenizer(description, return_tensors="pt").input_ids.to(device)
+prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+audio_arr = generation.cpu().numpy().squeeze()
+sf.write("ingrid_intense.wav", audio_arr, model.config.sampling_rate)
+```
+## Citation
+_coming later_
 **BibTeX:**
 **APA:**
 [More Information Needed]