Instructions to use techsword/wav2vec2-base-mandarin-magicdata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techsword/wav2vec2-base-mandarin-magicdata with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="techsword/wav2vec2-base-mandarin-magicdata")# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("techsword/wav2vec2-base-mandarin-magicdata") model = AutoModelForPreTraining.from_pretrained("techsword/wav2vec2-base-mandarin-magicdata", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 596 Bytes
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library_name: transformers
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- audio
- wav2vec2
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Fairseq wav2vec2-base pretraining checkpoint (checkpoint_best, ~85000 updates),
converted to HuggingFace format with the official transformers converter and
verified (weight-level spot check + forward-pass comparison against the
fairseq model; see conversion log). Pretrained on MAGICDATA (Mandarin Chinese).
Raw fairseq checkpoints: techsword/wav2vec2-base-mandarin-magicdata-checkpoints
Used in: tone-probe experiments, https://github.com/techsword/tone-encoding-in-speech-model
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