Sigurdur/vits_icelandic_rosa_female_monospeaker

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 Talrómur (see https://repository.clarin.is/repository/xmlui/handle/20.500.12537/330)

Model Details

Model Description

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: 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 = "Góðan daginn! Ég heiti Rósa, ég 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]

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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]

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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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Model Card Authors

Sigurdur Haukur Birgisson

Model Card Contact

Feel free to contact me through Linkedin: Sigurdur Haukur Birgisson