Automatic Speech Recognition
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
audio
speech
asr
hubert
Instructions to use voidful/tts_hubert_cluster_bart_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/tts_hubert_cluster_bart_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/tts_hubert_cluster_bart_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_cluster_bart_base") model = AutoModelForSeq2SeqLM.from_pretrained("voidful/tts_hubert_cluster_bart_base") - Notebooks
- Google Colab
- Kaggle
voidful/tts_hubert_cluster_bart_base
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_cluster_bart_base")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/tts_hubert_cluster_bart_base")
generate output
gen_output = model.generate(input_ids=tokenizer("going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards",return_tensors='pt').input_ids, max_length=1024)
print(tokenizer.decode(gen_output[0], skip_special_tokens=True))
Result
:vtok402::vtok329::vtok329::vtok75::vtok75::vtok75::vtok44::vtok150::vtok150::vtok222::vtok280::vtok280::vtok138::vtok409::vtok409::vtok409::vtok46::vtok441:
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