Checkout: transfer to different machine
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@@ -16,7 +16,7 @@ pip install -r requirements.txt
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To generate transcripts create a new job with your specifications (see jobs section below).
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To generate transcripts create a new job with your specifications (see jobs section below).
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<br/>
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<br/>
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If you need to download the transcriber model (not saved in `data/models`yet) set your huggingface token:
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If you need to download the transcriber model (not saved in `data/models` yet) set your huggingface token:
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On Linux/Mac:
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On Linux/Mac:
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@@ -52,11 +52,13 @@ Jobs specify tasks for transcript creation. A job contains the following informa
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- seperate data production form code
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- seperate data production form code
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- remember to accept the conditions on diarization if you use huggingface
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- remember to accept the conditions on diarization if you use huggingface
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- do a config json
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- do a config json
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- do option to have models already stored and point to them (is this necessary?)
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- do option to have models already stored and point to them (is this necessary? > if you do not want to interact with
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huggingface yes)
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- also for single language models
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- also for single language models
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- check warnings
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- check warnings
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- downloader for tib-av
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- downloader for tib-av
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- more optional setting
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- more optional setting
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- add progress bar
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## Structure
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## Structure
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+1
-1
@@ -80,7 +80,7 @@ class Transcriber:
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json.dump(self.transcript_output, f)
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json.dump(self.transcript_output, f)
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# Save just the transcripts to a .txt file
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# Save just the transcripts to a .txt file
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transcripts = [seg['value'] for seg in self.transcript_output['segments']]
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transcripts = [seg['text'] for seg in self.transcript_output['segments']]
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with open(output_txt_file, 'w') as f:
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with open(output_txt_file, 'w') as f:
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f.write('\n'.join(transcripts))
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f.write('\n'.join(transcripts))
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except Exception as e:
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except Exception as e:
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