library_name: transformers
datasets:
- Sigurdur/talromur-rosa
language:
- is
base_model:
- facebook/mms-tts
pipeline_tag: text-to-speech
Model Card for Model ID
This is a text-to-speach model for Icelandic, it is finetuned from facebook/mms-tts-isl with the dataset Talr贸mur (see https://repository.clarin.is/repository/xmlui/handle/20.500.12537/330)
Model Details
Model Description
This is the model card of a 馃 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Sigurdur Haukur Birgisson
- Model type: VITS
- Language(s) (NLP): Icelandic, isl
- License: [More Information Needed]
- Finetuned from model: facebook/mms-tts-isl
Uses
This model should be used for text-to-speach applications for Icelandic.
Direct Use
from transformers import VitsModel, AutoTokenizer
import scipy.io.wavfile as wav
import torch
model = VitsModel.from_pretrained("Sigurdur/vits_icelandic_rosa_female_monospeaker")
tokenizer = AutoTokenizer.from_pretrained("Sigurdur/vits_icelandic_rosa_female_monospeaker")
text = "G贸冒an daginn! 脡g heiti R贸sa, 茅g er talgervill"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(**inputs).waveform
sampling_rate = getattr(sampling_rate, "sampling_rate", 16000) # Default to 16kHz if not set
if not (0 <= sampling_rate <= 65535):
raise ValueError(f"Invalid sampling rate: {sampling_rate}")
waveform = output.squeeze().cpu().numpy() # Remove batch dimension if present
To save output to file
wav.write("output.wav", rate=sampling_rate, data=waveform)
To view in jupyter notebook
from IPython.display import Audio
# show audio player for "output.wav"
Audio(output, rate=sampling_rate)
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.
Training Data
[More Information Needed]
Training Hyperparameters
- Training regime: fp16
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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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
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors
Sigurdur Haukur Birgisson
Model Card Contact
Feel free to contact me through Linkedin: Sigurdur Haukur Birgisson