Automatic Speech Recognition
Transformers
Safetensors
DiCoW
speech
whisper
multilingual
speaker-diarization
meeting-transcription
target-speaker-asr
BUT-FIT
custom_code
Instructions to use BUT-FIT/DiCoW_v3_3_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v3_3_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="BUT-FIT/DiCoW_v3_3_large", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("BUT-FIT/DiCoW_v3_3_large", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update generation_config.json
#2
by asiiiiir0105 - opened
- generation_config.json +1 -1
generation_config.json
CHANGED
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@@ -29,7 +29,7 @@
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"apply_fddt_to_n_layers": -1,
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"apply_target_amp_to_n_layers": -1,
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"attn_implementation": "flash_attention_2",
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-
"begin_suppress_tokens": null
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| 33 |
"bos_token_id": 50257,
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"ctc_margin": 0,
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"ctc_weight": 0,
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"apply_fddt_to_n_layers": -1,
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| 30 |
"apply_target_amp_to_n_layers": -1,
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"attn_implementation": "flash_attention_2",
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+
"begin_suppress_tokens": null,
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| 33 |
"bos_token_id": 50257,
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| 34 |
"ctc_margin": 0,
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"ctc_weight": 0,
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