# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_unispeech_s149")
model = AutoModelForCTC.from_pretrained("jonatasgrosman/exp_w2v2t_sv-se_unispeech_s149", device_map="auto")Quick Links
YAML Metadata Error:"language[0]" must only contain lowercase characters
YAML Metadata Error:"language[0]" with value "sv-SE" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
exp_w2v2t_sv-se_unispeech_s149
Fine-tuned microsoft/unispeech-large-1500h-cv for speech recognition using the train split of Common Voice 7.0 (sv-SE). When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/exp_w2v2t_sv-se_unispeech_s149")