Any-to-Any
Transformers
Safetensors
multilingual
minicpmo
feature-extraction
minicpm-o
omni
vision
ocr
multi-image
video
custom_code
audio
speech
voice cloning
live Streaming
realtime speech conversation
asr
tts
Instructions to use openbmb/MiniCPM-o-2_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-o-2_6 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-o-2_6", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
fix audio batch inference
Browse files- modeling_minicpmo.py +1 -1
modeling_minicpmo.py
CHANGED
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@@ -589,7 +589,7 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
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| 589 |
audio_start_pos = 0
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| 590 |
for bound in audio_bounds[i]:
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| 591 |
audio_len = bound[1] - bound[0]
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| 592 |
-
input_embeddings[
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| 593 |
audio_start_pos : audio_start_pos + audio_len, :
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| 594 |
]
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| 595 |
audio_start_pos += audio_len
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| 589 |
audio_start_pos = 0
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| 590 |
for bound in audio_bounds[i]:
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| 591 |
audio_len = bound[1] - bound[0]
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| 592 |
+
input_embeddings[i, bound[0] : bound[1]] = audio_embs[
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| 593 |
audio_start_pos : audio_start_pos + audio_len, :
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| 594 |
]
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| 595 |
audio_start_pos += audio_len
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