Upload openbmb_VoxCPM1.5_0.py with huggingface_hub

openbmb_VoxCPM1.5_0.py

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+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "numpy",
+#     "einops",
+#     "pandas",
+#     "matplotlib",
+#     "protobuf",
+#     "torch",
+#     "sentencepiece",
+#     "torchvision",
+#     "transformers",
+#     "timm",
+#     "diffusers",
+#     "sentence-transformers",
+#     "accelerate",
+#     "peft",
+#     "slack-sdk",
+# ]
+# ///
+
+try:
+    import soundfile as sf
+    from voxcpm import VoxCPM
+    
+    model = VoxCPM.from_pretrained("openbmb/VoxCPM1.5")
+    
+    wav = model.generate(
+        text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.",
+        prompt_wav_path=None,      # optional: path to a prompt speech for voice cloning
+        prompt_text=None,          # optional: reference text
+        cfg_value=2.0,             # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
+        inference_timesteps=10,   # LocDiT inference timesteps, higher for better result, lower for fast speed
+        normalize=True,           # enable external TN tool
+        denoise=True,             # enable external Denoise tool
+        retry_badcase=True,        # enable retrying mode for some bad cases (unstoppable)
+        retry_badcase_max_times=3,  # maximum retrying times
+        retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
+    )
+    
+    sf.write("output.wav", wav, 16000)
+    print("saved: output.wav")
+    with open('openbmb_VoxCPM1.5_0.txt', 'w', encoding='utf-8') as f:
+        f.write('Everything was good in openbmb_VoxCPM1.5_0.txt')
+except Exception as e:
+    import os
+    from slack_sdk import WebClient
+    client = WebClient(token=os.environ['SLACK_TOKEN'])
+    client.chat_postMessage(
+        channel='#hub-model-metadata-snippets-sprint',
+        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/openbmb_VoxCPM1.5_0.txt|openbmb_VoxCPM1.5_0.txt>',
+    )
+
+    with open('openbmb_VoxCPM1.5_0.txt', 'a', encoding='utf-8') as f:
+        import traceback
+        f.write('''```CODE: 
+import soundfile as sf
+from voxcpm import VoxCPM
+
+model = VoxCPM.from_pretrained("openbmb/VoxCPM1.5")
+
+wav = model.generate(
+    text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.",
+    prompt_wav_path=None,      # optional: path to a prompt speech for voice cloning
+    prompt_text=None,          # optional: reference text
+    cfg_value=2.0,             # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
+    inference_timesteps=10,   # LocDiT inference timesteps, higher for better result, lower for fast speed
+    normalize=True,           # enable external TN tool
+    denoise=True,             # enable external Denoise tool
+    retry_badcase=True,        # enable retrying mode for some bad cases (unstoppable)
+    retry_badcase_max_times=3,  # maximum retrying times
+    retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
+)
+
+sf.write("output.wav", wav, 16000)
+print("saved: output.wav")
+```
+
+ERROR: 
+''')
+        traceback.print_exc(file=f)
+    
+finally:
+    from huggingface_hub import upload_file
+    upload_file(
+        path_or_fileobj='openbmb_VoxCPM1.5_0.txt',
+        repo_id='model-metadata/code_execution_files',
+        path_in_repo='openbmb_VoxCPM1.5_0.txt',
+        repo_type='dataset',
+    )