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Runtime error
Runtime error
Update
Browse files- app.py +9 -2
- requirements.txt +1 -0
app.py
CHANGED
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@@ -200,7 +200,12 @@ def tts(
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edge_output_filename,
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(tgt_sr, audio_opt),
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)
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-
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except:
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info = traceback.format_exc()
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print(info)
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@@ -214,11 +219,13 @@ This is a text-to-speech demo of RVC moe models of [rvc_okiba](https://huggingfa
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Input text β‘[(edge-tts)](https://github.com/rany2/edge-tts)β‘ Speech mp3 file β‘[(RVC)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)β‘ Final output
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Although the models are trained on Japanese voices and intended for Japanese text, they can also be used with other languages with the corresponding edge-tts speaker (but possibly with a Japanese accent).
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Input characters are limited to 280 characters, and the speech audio is limited to 20 seconds in this π€ space.
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[Visit this GitHub repo](https://github.com/litagin02/rvc-tts-webui) for running locally with your models!
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"""
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app = gr.Blocks()
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edge_output_filename,
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(tgt_sr, audio_opt),
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)
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except EOFError:
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info = """
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It seems that edge-tts output is empty. This may occur when the input text and the speaker do not match.
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For example, maybe you entered Japanese (without alphabets) text but chose non-Japanese speaker?"""
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print(info)
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return info, None, None
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except:
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info = traceback.format_exc()
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print(info)
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Input text β‘[(edge-tts)](https://github.com/rany2/edge-tts)β‘ Speech mp3 file β‘[(RVC)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)β‘ Final output
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This runs on the π€ server's cpu, so it may be slow.
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Although the models are trained on Japanese voices and intended for Japanese text, they can also be used with other languages with the corresponding edge-tts speaker (but possibly with a Japanese accent).
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Input characters are limited to 280 characters, and the speech audio is limited to 20 seconds in this π€ space.
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+
[Visit this GitHub repo](https://github.com/litagin02/rvc-tts-webui) for running locally with your models and GPU!
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"""
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app = gr.Blocks()
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requirements.txt
CHANGED
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@@ -2,6 +2,7 @@ edge_tts==6.1.7
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fairseq==0.12.2
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faiss_cpu==1.7.4
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gradio==3.36.1
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librosa==0.9.1
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numpy==1.23.5
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praat-parselmouth==0.4.3
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fairseq==0.12.2
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faiss_cpu==1.7.4
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gradio==3.36.1
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gradio_client==0.2.9
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librosa==0.9.1
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numpy==1.23.5
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praat-parselmouth==0.4.3
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