| Filename,transcription,audio | |
| audiofile29.wav,this is the last sentence in this dataset so thank you,"{'array': array([-0.00154497, -0.00154497, -0.00154497, ..., -0.00154497, | |
| -0.00154497, -0.00154497], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile29.wav', 'sampling_rate': 16000}" | |
| audiofile25.wav,we are using shared memory for matrix multiplication,"{'array': array([-0.0013941, -0.0013941, -0.0013941, ..., -0.0013941, -0.0013941, | |
| -0.0013941], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile25.wav', 'sampling_rate': 16000}" | |
| audiofile13.wav,so i asked my friends also to record voice and send it to me,"{'array': array([-0.00135984, -0.00135984, -0.00135984, ..., -0.00135984, | |
| -0.00135984, -0.00135984], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile13.wav', 'sampling_rate': 16000}" | |
| audiofile1.wav,good morning everyone,"{'array': array([-9.7171287e-05, -9.7171287e-05, -9.7171287e-05, ..., | |
| -9.7171287e-05, 3.4426435e-04, 7.8569993e-04], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile1.wav', 'sampling_rate': 16000}" | |
| audiofile5.wav,train data is used to train the deep learning model,"{'array': array([-0.00258282, -0.00258282, -0.00258282, ..., -0.00278775, | |
| -0.00258282, -0.00278775], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile5.wav', 'sampling_rate': 16000}" | |
| audiofile17.wav,in shared memory consider there are lot of threads in a thread block,"{'array': array([-0.0011769, -0.0011769, -0.0011769, ..., -0.0011769, -0.0011769, | |
| -0.0011769], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile17.wav', 'sampling_rate': 16000}" | |
| audiofile6.wav,the validation dataset is used to fine tune the trained model and reduce error,"{'array': array([-0.0019547, -0.0019547, -0.0019547, ..., -0.0019547, -0.0019547, | |
| -0.0019547], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile6.wav', 'sampling_rate': 16000}" | |
| audiofile14.wav,since it is not their sole duty im also indulging in dataset creation,"{'array': array([6.8441186e-05, 6.8441186e-05, 6.8441186e-05, ..., 6.8441186e-05, | |
| 6.8441186e-05, 6.8441186e-05], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile14.wav', 'sampling_rate': 16000}" | |
| audiofile12.wav,model was working fine for the sample dataset but my accent was not recognising,"{'array': array([-0.00075871, -0.00075871, -0.00075871, ..., -0.00075871, | |
| -0.00075871, -0.00075871], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile12.wav', 'sampling_rate': 16000}" | |
| audiofile23.wav,after that it will do commutation on shared memory,"{'array': array([-0.00209466, -0.00209466, -0.00209466, ..., -0.00235505, | |
| -0.00235505, -0.00209466], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile23.wav', 'sampling_rate': 16000}" | |
| audiofile2.wav,it is always nice to meet you in a fresh mood,"{'array': array([ 2.5861387e-05, 2.5861387e-05, 2.5861387e-05, ..., | |
| 2.6418168e-02, 1.2057647e-02, -8.1247035e-03], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile2.wav', 'sampling_rate': 16000}" | |
| audiofile3.wav,this is the custom dataset which is used to train the wave2vec model,"{'array': array([-0.00024371, -0.00024371, -0.00024371, ..., -0.02418295, | |
| -0.02207067, -0.00024371], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile3.wav', 'sampling_rate': 16000}" | |
| audiofile26.wav,there are two shared arrays since we are using two memory locations,"{'array': array([-0.00214535, -0.00214535, -0.00214535, ..., -0.00240758, | |
| -0.00214535, -0.00240758], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile26.wav', 'sampling_rate': 16000}" | |
| audiofile4.wav,this consist of test train and validation data included,"{'array': array([-0.00096769, -0.00096769, -0.00096769, ..., -0.00096769, | |
| -0.00096769, -0.00096769], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile4.wav', 'sampling_rate': 16000}" | |
| audiofile22.wav,it is necessory to preload a small block from input array,"{'array': array([-0.00164573, -0.00164573, -0.00164573, ..., -0.00191057, | |
| -0.00191057, -0.00164573], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile22.wav', 'sampling_rate': 16000}" | |
| audiofile27.wav,one of our bro is installing the python libraries for doing the program along with the workshop,"{'array': array([-0.00197866, -0.00197866, -0.00197866, ..., -0.00197866, | |
| -0.00197866, -0.00197866], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile27.wav', 'sampling_rate': 16000}" | |
| audiofile19.wav,we can tell gpu how much memory we can use as cache,"{'array': array([-0.00142616, -0.00142616, -0.00142616, ..., -0.00142616, | |
| -0.00142616, -0.00142616], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile19.wav', 'sampling_rate': 16000}" | |
| audiofile30.wav,so this are the sentences that would turn the model into a better one,"{'array': array([-0.00090364, -0.00090364, -0.00090364, ..., -0.00120979, | |
| -0.00120979, -0.00120979], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile30.wav', 'sampling_rate': 16000}" | |
| audiofile21.wav,memory is allocated once during the duration of the kernal,"{'array': array([-0.00097256, -0.00097256, -0.00097256, ..., -0.00097256, | |
| -0.00128039, -0.00128039], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile21.wav', 'sampling_rate': 16000}" | |
| audiofile8.wav,these are recorded personally by me,"{'array': array([-0.00147271, -0.00147271, -0.00147271, ..., -0.00147271, | |
| -0.00147271, -0.00147271], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile8.wav', 'sampling_rate': 16000}" | |
| audiofile11.wav,the model from facebok was identified after a lot of research and preperation,"{'array': array([-0.00012905, -0.00012905, -0.00012905, ..., -0.00012905, | |
| -0.000616 , -0.000616 ], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile11.wav', 'sampling_rate': 16000}" | |
| audiofile15.wav,we are having a session based on cuda programming,"{'array': array([-0.00074556, -0.00074556, -0.00074556, ..., -0.00074556, | |
| -0.00074556, -0.00074556], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile15.wav', 'sampling_rate': 16000}" | |
| audiofile20.wav,we are passing two arguments shape and time,"{'array': array([-0.00225772, -0.00225772, -0.00225772, ..., -0.00225772, | |
| -0.00225772, -0.00225772], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile20.wav', 'sampling_rate': 16000}" | |
| audiofile7.wav,the test data is used to test the accuracy,"{'array': array([-0.00150242, -0.00150242, -0.00150242, ..., -0.00150242, | |
| -0.00150242, -0.00150242], dtype=float32), 'path': '/home/ba-anandhus/Documents/fine tuning/mono/audiofile7.wav', 'sampling_rate': 16000}" | |