tonypeng/whisper-finetuning
Automatic Speech Recognition
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audioduration (s) 1.62
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1
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data/train/audio_train_1.wav
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The API receives millions of requests daily.
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2
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data/train/audio_train_2.wav
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The server's gone down again.
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3
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data/train/audio_train_3.wav
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The model was trained on a billion parameters.
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4
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data/train/audio_train_4.wav
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The logs seem fine to me.
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5
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data/train/audio_train_5.wav
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Let's create a Docker container for this.
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6
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data/train/audio_train_6.wav
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The bug has some complex edge cases.
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7
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data/train/audio_train_7.wav
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The system handles a billion requests per day.
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8
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data/train/audio_train_8.wav
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The cache never expires.
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9
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data/train/audio_train_9.wav
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The project dependencies need updating.
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10
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data/train/audio_train_10.wav
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The algorithm is very efficient.
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11
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data/train/audio_train_11.wav
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The server would sing an error message when overloaded.
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12
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data/train/audio_train_12.wav
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The visualization module crashed again.
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13
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data/train/audio_train_13.wav
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Keep track of these dependencies.
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14
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data/train/audio_train_14.wav
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The software crashed again.
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15
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data/train/audio_train_15.wav
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The framework handles routing like magic.
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16
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data/train/audio_train_16.wav
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The server had connection issues.
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17
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data/train/audio_train_17.wav
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Filter them out.
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18
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data/train/audio_train_18.wav
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The memory leak is finally gone.
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19
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data/train/audio_train_19.wav
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The notification sound has a warning tone.
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20
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data/train/audio_train_20.wav
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First deploy, then test.
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21
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data/train/audio_train_21.wav
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The old API endpoint is gone now.
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22
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data/train/audio_train_22.wav
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My friend crashed the production server accidentally.
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23
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data/train/audio_train_23.wav
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These errors seem to occur randomly.
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24
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data/train/audio_train_24.wav
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Keep pushing to the repo.
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25
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data/train/audio_train_25.wav
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Process them in batches.
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26
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data/train/audio_train_26.wav
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Train it, then validate.
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27
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data/train/audio_train_27.wav
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Check the buffer sizes in the config.
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28
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data/train/audio_train_28.wav
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That's a friendly request from the new developer.
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29
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data/train/audio_train_29.wav
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Kill all processes.
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30
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data/train/audio_train_30.wav
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Map them to objects.
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31
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data/train/audio_train_31.wav
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These batch sizes are too large for our GPU.
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32
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data/train/audio_train_32.wav
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Run the script, then check logs.
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33
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data/train/audio_train_33.wav
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Delete all temp files.
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34
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data/train/audio_train_34.wav
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Keep these variables in scope.
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35
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data/train/audio_train_35.wav
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What about the edge cases?
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36
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data/train/audio_train_36.wav
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Can you be a friend and review my pull request?
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37
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data/train/audio_train_37.wav
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Load the data, then transform it.
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38
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data/train/audio_train_38.wav
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Tell me about the new feature.
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39
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data/train/audio_train_39.wav
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We need various sizes for testing.
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40
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data/train/audio_train_40.wav
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Select all records.
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41
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data/train/audio_train_41.wav
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These numbers seem off somehow.
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42
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data/train/audio_train_42.wav
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Check all connections.
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43
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data/train/audio_train_43.wav
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The array sizes don't match up.
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44
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data/train/audio_train_44.wav
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Cache them locally first.
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45
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data/train/audio_train_45.wav
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Did all tests pass?
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46
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data/train/audio_train_46.wav
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Keep this branch updated.
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47
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data/train/audio_train_47.wav
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Hey friend, could you check these logs?
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48
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data/train/audio_train_48.wav
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We need to keep the server running.
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49
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data/train/audio_train_49.wav
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My friend helped me debug this nasty code yesterday.
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50
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data/train/audio_train_50.wav
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How about we use Docker?
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51
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data/train/audio_train_51.wav
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Compile first, then execute.
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52
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data/train/audio_train_52.wav
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The latency spikes seem concerning.
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53
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data/train/audio_train_53.wav
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It's about time we refactored this.
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54
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data/train/audio_train_54.wav
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I'm not friendly with this new framework yet.
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55
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data/train/audio_train_55.wav
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The reason for caching is performance.
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56
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data/train/audio_train_56.wav
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When did you push the code?
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57
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data/train/audio_train_57.wav
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The file won't read properly.
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58
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data/train/audio_train_58.wav
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The bug is gone after the latest patch.
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59
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data/train/audio_train_59.wav
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Let's set the right tone in our documentation.
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60
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data/train/audio_train_60.wav
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Don't use magic numbers in the code.
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61
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data/train/audio_train_61.wav
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The chatbot's tone needs adjustment.
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62
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data/train/audio_train_62.wav
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When should we deploy the changes?
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63
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data/train/audio_train_63.wav
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Read the config file first.
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64
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data/train/audio_train_64.wav
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That's not magic, it's just good architecture.
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65
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data/train/audio_train_65.wav
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Find the reason for the timeout.
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66
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data/train/audio_train_66.wav
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The scanner can't read this QR code.
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67
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data/train/audio_train_67.wav
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Click on the submit button.
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68
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data/train/audio_train_68.wav
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The log shows when users log in.
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69
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data/train/audio_train_69.wav
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Let me know when the build is done.
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70
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data/train/audio_train_70.wav
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Can't read from the database.
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71
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data/train/audio_train_71.wav
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All my cached data is gone.
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72
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data/train/audio_train_72.wav
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Give me a reason to use this library.
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73
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data/train/audio_train_73.wav
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That feature is gone in version 2.0.
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74
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data/train/audio_train_74.wav
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There's no magic in programming.
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75
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data/train/audio_train_75.wav
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Turn the server on before running the tests.
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76
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data/train/audio_train_76.wav
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What's the reason for this null pointer?
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77
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data/train/audio_train_77.wav
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The ML model training is still on.
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78
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data/train/audio_train_78.wav
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Remember when we used PHP?
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79
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data/train/audio_train_79.wav
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When did the server crash?
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80
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data/train/audio_train_80.wav
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Keep a casual tone in the user guides.
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81
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data/train/audio_train_81.wav
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Did you read the documentation?
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82
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data/train/audio_train_82.wav
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Let's read the error logs.
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83
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data/train/audio_train_83.wav
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The bug fix worked like magic.
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84
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data/train/audio_train_84.wav
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That's the reason we need tests.
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85
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data/train/audio_train_85.wav
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There's a reason we use TypeScript.
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86
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data/train/audio_train_86.wav
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Avoid magic strings in your constants.
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87
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data/train/audio_train_87.wav
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The API is very unstable.
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88
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data/train/audio_train_88.wav
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Try running it in debug mode.
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89
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data/train/audio_train_89.wav
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Past iterations were slower.
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90
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data/train/audio_train_90.wav
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The server never responded.
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91
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data/train/audio_train_91.wav
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We tried multiple algorithms.
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92
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data/train/audio_train_92.wav
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Don't ruin the API documentation.
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93
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data/train/audio_train_93.wav
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The ML model is running on GPU.
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94
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data/train/audio_train_94.wav
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Memory usage is very high.
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95
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data/train/audio_train_95.wav
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Make room for the new database instance.
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96
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data/train/audio_train_96.wav
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Bad commits can ruin the main branch.
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97
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data/train/audio_train_97.wav
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Past commits show the issue.
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98
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data/train/audio_train_98.wav
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Never ignore security warnings.
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99
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data/train/audio_train_99.wav
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The team tried the new framework.
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100
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data/train/audio_train_100.wav
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That's a very complex query.
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End of preview. Expand
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Size of downloaded dataset files:
362 MB
Size of the auto-converted Parquet files:
362 MB
Number of rows:
1,057
Models trained or fine-tuned on tonypeng/whisper-finetuning