Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Portuguese
English
Spanish
whisper
audio
Eval Results (legacy)
Instructions to use cloudqi/cqi_speech_recognize_pt_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudqi/cqi_speech_recognize_pt_v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cloudqi/cqi_speech_recognize_pt_v0")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cloudqi/cqi_speech_recognize_pt_v0") model = AutoModelForSpeechSeq2Seq.from_pretrained("cloudqi/cqi_speech_recognize_pt_v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33b368e3de2fd9504f151f26b6b8a44a6288d3bdbdda1498e1924fa510855c6b
- Size of remote file:
- 3.06 GB
- SHA256:
- 96d734d68ad5d63c8f41d525f5769788432f6963f32dbe36feefaa33d736a962
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.