Filename
stringclasses 24
values | transcription
stringclasses 24
values | audio
stringclasses 24
values |
---|---|---|
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} |