metadata
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-2
See Emotive Icelandic 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-2, corresponds to a model with a refined subset of the original training corpus. The model can be, additionally, prompted
with the target setting of voice intensity:
- low intensity: voice is low expressive
- medium intensity: voice is somewhat expressive
- high intensity: voice is very expressive
Usage
Use the code below to get started with the model.
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-2").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:
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APA:
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