Using python to Transform a JSON output

Question

Using watson speech to text i have been getting the following output.

{'results': [{'alternatives': [{'timestamps': [['I', 0.09, 0.16], ["can't", 0.16, 0.48], ['even', 0.48, 0.67], ['believe', 0.67, 0.96], ['Michigan', 0.96, 1.45], ['went', 1.45, 1.74], ['for', 1.74, 1.87], ['two', 1.87, 2.1], ['against', 2.1, 2.44], ['always', 2.44, 2.72], ['you', 2.72, 3.05], ['should', 3.29, 3.55], ['just', 3.55, 3.72], ['play', 3.72, 3.89], ['it', 3.89, 4.02], ['safe', 4.02, 4.24], ['with', 4.24, 4.36], ['a', 4.36, 4.4], ['field', 4.4, 4.77], ['goal', 4.77, 5.04], ['whatever', 5.09, 5.47], ['you', 5.47, 5.62], ['said', 5.62, 5.9], ['that', 5.9, 6.12], ['own', 6.62, 6.89], ['aging', 6.89, 7.23], ['Pearson', 7.23, 7.95], ['is', 7.98, 8.14], ['ten', 8.14, 8.36], ['thousand', 8.36, 8.72], ['yard', 8.72, 8.99], ['record', 8.99, 9.5]], 'confidence': 0.752, 'transcript': "I can't even believe Michigan went for two against always you should just play it safe with a field goal whatever you said that own aging Pearson is ten thousand yard record "}], 'final': True}, {'alternatives': [{'timestamps': [['while', 10.34, 11.06]], 'confidence': 0.417, 'transcript': 'while '}], 'final': True}, {'alternatives': [{'timestamps': [['yeah', 12.3, 12.81], ['sure', 13.34, 13.78]], 'confidence': 0.556, 'transcript': 'yeah sure '}], 'final': True}, {'alternatives': [{'timestamps': [['and', 14.42, 14.73], ["here's", 14.73, 15.12], ['our', 15.12, 15.21], ['calamari', 15.21, 15.86], ['Sir', 15.86, 16.24]], 'confidence': 0.959, 'transcript': "and here's our calamari Sir "}], 'final': True}, {'alternatives': [{'timestamps': [['faster', 17.56, 17.97], ['wow', 18.01, 18.36], ['this', 18.36, 18.55], ['food', 18.55, 18.78], ['looks', 18.78, 18.92], ['amazing', 18.92, 19.53], ['thank', 19.56, 19.82], ['you', 19.82, 20.03], ['south', 20.48, 21.02], ['let', 21.17, 21.33], ['me', 21.33, 21.46], ['just', 21.46, 21.71], ['tell', 21.71, 21.92], ['you', 21.92, 22.16], ['yesterday', 22.37, 23.12], ['when', 23.15, 23.37], ['means', 23.37, 23.73], ['easy', 23.73, 24.01], ['at', 24.01, 24.15], ['blackfriday', 24.15, 24.59], ['shopping', 24.59, 25.13], ['like', 25.19, 25.51], ['L.', 25.59, 25.82], ['M.', 25.82, 26.15], ['fricking', 26.18, 26.58], ['Gee', 26.58, 26.97], ['it', 27.0, 27.15], ['was', 27.15, 27.37], ['cruel', 27.53, 27.81], ['Razi', 27.97, 28.73], ['we', 28.85, 29.08], ['started', 29.08, 29.54], ['me', 29.54, 29.73], ['sis', 29.73, 30.04], ['and', 30.04, 30.17], ['like', 30.17, 30.47]], 'confidence': 0.648, 'transcript': 'faster wow this food looks amazing thank you south let me just tell you yesterday when means easy at blackfriday shopping like L. M. fricking Gee it was cruel Razi we started me sis and like '}], 'final': True}, {'alternatives': [{'timestamps': [['the', 31.31, 31.39], ['sales', 31.39, 31.77], ['were', 31.77, 32.03], ['horrendous', 32.09, 32.93], ['so', 33.19, 33.37], ['then', 33.37, 33.81], ['we', 33.88, 34.17], ['went', 34.17, 34.52], ['to', 34.52, 34.98], ['Charlotte', 35.01, 35.46], ['russe', 35.46, 35.81], ['and', 35.81, 35.97], ['let', 35.97, 36.08], ['me', 36.08, 36.17], ['tell', 36.17, 36.38], ['you', 36.38, 36.51], ['I', 36.51, 36.63], ['got', 36.63, 36.87], ['some', 36.87, 37.11], ['killer', 37.25, 37.86], ['deals', 37.89, 38.35], ['there', 38.35, 38.73], ['are', 39.13, 39.27], ['you', 39.27, 39.49], ['listening', 39.8, 40.33], ['to', 40.33, 40.47], ['everything', 40.47, 40.91], ["I'm", 40.91, 41.05], ['saying', 41.05, 41.61], ['yeah', 42.03, 42.39], ['yeah', 42.39, 42.8], ['no', 42.83, 43.2], ['of', 43.26, 43.37], ['course', 43.37, 43.85], ['not', 43.88, 44.12], ["it's", 44.12, 44.24], ['just', 44.24, 44.4], ['that', 44.4, 44.63], ['this', 44.63, 44.84], ['food', 44.84, 45.26], ['is', 45.26, 45.51], ['absolutely', 45.54, 46.17], ['fantastic', 46.17, 47.03], ['and', 47.35, 47.62], ['I', 47.62, 47.72], ['think', 47.72, 47.92], ['I', 47.92, 47.97], ['need', 47.97, 48.09], ['to', 48.09, 48.16], ['go', 48.16, 48.26], ['complement', 48.26, 48.71], ['the', 48.71, 48.77], ['chef', 48.77, 49.21]], 'confidence': 0.857, 'transcript': "the sales were horrendous so then we went to Charlotte russe and let me tell you I got some killer deals there are you listening to everything I'm saying yeah yeah no of course not it's just that this food is absolutely fantastic and I think I need to go complement the chef "}], 'final': True}, {'alternatives': [{'timestamps': [['our', 49.9, 50.04], ['son', 50.04, 50.5], ['son', 50.77, 51.21]], 'confidence': 0.491, 'transcript': 'our son son '}], 'final': True}, {'alternatives': [{'timestamps': [['yes', 51.8, 52.04], ['Sir', 52.04, 52.25], ['what', 52.25, 52.39], ['can', 52.39, 52.51], ['I', 52.51, 52.55], ['do', 52.55, 52.7], ['for', 52.7, 52.85], ['you', 52.85, 53.08], ["I'm", 53.44, 53.95], ['in', 53.98, 54.16], ['love', 54.16, 54.56], ['with', 54.56, 54.68], ['this', 54.68, 54.82], ['calamari', 54.82, 55.4], ['please', 55.4, 55.7], ['just', 55.7, 55.96], ['give', 56.21, 56.39], ['my', 56.39, 56.5], ['regards', 56.5, 56.92], ['the', 56.92, 57.03], ['shaft', 57.03, 57.38], ['of', 57.67, 57.87], ['course', 57.87, 58.39], ['Sir', 58.39, 58.63], ['we', 58.63, 58.77], ['love', 58.77, 59.2], ['to', 59.2, 59.3], ['hear', 59.3, 59.53], ['that', 59.53, 59.88], ['thank', 60.05, 60.35], ['you', 60.35, 60.47], ['again', 60.47, 60.85], ['for', 60.85, 61.01], ['your', 61.01, 61.15], ['business', 61.15, 61.87]], 'confidence': 0.92, 'transcript': "yes Sir what can I do for you I'm in love with this calamari please just give my regards the shaft of course Sir we love to hear that thank you again for your business "}], 'final': True}], 'result_index': 0, 'speaker_labels': [{'from': 0.09, 'to': 0.16, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 0.16, 'to': 0.48, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 0.48, 'to': 0.67, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 0.67, 'to': 0.96, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 0.96, 'to': 1.45, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 1.45, 'to': 1.74, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 1.74, 'to': 1.87, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 1.87, 'to': 2.1, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 2.1, 'to': 2.44, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 2.44, 'to': 2.72, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 2.72, 'to': 3.05, 'speaker': 0, 'confidence': 0.413, 'final': False}, {'from': 3.29, 'to': 3.55, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 3.55, 'to': 3.72, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 3.72, 'to': 3.89, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 3.89, 'to': 4.02, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 4.02, 'to': 4.24, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 4.24, 'to': 4.36, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 4.36, 'to': 4.4, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 4.4, 'to': 4.77, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 4.77, 'to': 5.04, 'speaker': 0, 'confidence': 0.448, 'final': False}, {'from': 5.09, 'to': 5.47, 'speaker': 1, 'confidence': 0.527, 'final': False}, {'from': 5.47, 'to': 5.62, 'speaker': 1, 'confidence': 0.527, 'final': False}, {'from': 5.62, 'to': 5.9, 'speaker': 1, 'confidence': 0.527, 'final': False}, {'from': 5.9, 'to': 6.12, 'speaker': 1, 'confidence': 0.527, 'final': False}, {'from': 6.62, 'to': 6.89, 'speaker': 0, 'confidence': 0.561, 'final': False}, {'from': 6.89, 'to': 7.23, 'speaker': 0, 'confidence': 0.561, 'final': False}, {'from': 7.23, 'to': 7.95, 'speaker': 0, 'confidence': 0.561, 'final': False}, {'from': 7.98, 'to': 8.14, 'speaker': 0, 'confidence': 0.501, 'final': False}, {'from': 8.14, 'to': 8.36, 'speaker': 0, 'confidence': 0.501, 'final': False}, {'from': 8.36, 'to': 8.72, 'speaker': 0, 'confidence': 0.501, 'final': False}, {'from': 8.72, 'to': 8.99, 'speaker': 0, 'confidence': 0.501, 'final': False}, {'from': 8.99, 'to': 9.5, 'speaker': 0, 'confidence': 0.501, 'final': False}, {'from': 10.34, 'to': 11.06, 'speaker': 1, 'confidence': 0.381, 'final': False}, {'from': 12.3, 'to': 12.81, 'speaker': 1, 'confidence': 0.462, 'final': False}, {'from': 13.34, 'to': 13.78, 'speaker': 1, 'confidence': 0.448, 'final': False}, {'from': 14.42, 'to': 14.73, 'speaker': 0, 'confidence': 0.369, 'final': False}, {'from': 14.73, 'to': 15.12, 'speaker': 0, 'confidence': 0.369, 'final': False}, {'from': 15.12, 'to': 15.21, 'speaker': 0, 'confidence': 0.369, 'final': False}, {'from': 15.21, 'to': 15.86, 'speaker': 0, 'confidence': 0.369, 'final': False}, {'from': 15.86, 'to': 16.24, 'speaker': 0, 'confidence': 0.369, 'final': False}, {'from': 17.56, 'to': 17.97, 'speaker': 1, 'confidence': 0.424, 'final': False}, {'from': 18.01, 'to': 18.36, 'speaker': 0, 'confidence': 0.623, 'final': False}, {'from': 18.36, 'to': 18.55, 'speaker': 0, 'confidence': 0.623, 'final': False}, {'from': 18.55, 'to': 18.78, 'speaker': 0, 'confidence': 0.623, 'final': False}, {'from': 18.78, 'to': 18.92, 'speaker': 0, 'confidence': 0.623, 'final': False}, {'from': 18.92, 'to': 19.53, 'speaker': 0, 'confidence': 0.623, 'final': False}, {'from': 19.56, 'to': 19.82, 'speaker': 0, 'confidence': 0.614, 'final': False}, {'from': 19.82, 'to': 20.03, 'speaker': 0, 'confidence': 0.614, 'final': False}, {'from': 20.48, 'to': 21.02, 'speaker': 1, 'confidence': 0.446, 'final': False}, {'from': 21.17, 'to': 21.33, 'speaker': 1, 'confidence': 0.458, 'final': False}, {'from': 21.33, 'to': 21.46, 'speaker': 1, 'confidence': 0.458, 'final': False}, {'from': 21.46, 'to': 21.71, 'speaker': 1, 'confidence': 0.458, 'final': False}, {'from': 21.71, 'to': 21.92, 'speaker': 1, 'confidence': 0.458, 'final': False}, {'from': 21.92, 'to': 22.16, 'speaker': 1, 'confidence': 0.458, 'final': False}, {'from': 22.37, 'to': 23.12, 'speaker': 1, 'confidence': 0.563, 'final': False}, {'from': 23.15, 'to': 23.37, 'speaker': 1, 'confidence': 0.612, 'final': False}, {'from': 23.37, 'to': 23.73, 'speaker': 1, 'confidence': 0.612, 'final': False}, {'from': 23.73, 'to': 24.01, 'speaker': 1, 'confidence': 0.612, 'final': False}, {'from': 24.01, 'to': 24.15, 'speaker': 1, 'confidence': 0.612, 'final': False}, {'from': 24.15, 'to': 24.59, 'speaker': 1, 'confidence': 0.612, 'final': False}, {'from': 24.59, 'to': 25.13, 'speaker': 1, 'confidence': 0.612, 'final': False}, {'from': 25.19, 'to': 25.51, 'speaker': 1, 'confidence': 0.568, 'final': False}, {'from': 25.59, 'to': 25.82, 'speaker': 1, 'confidence': 0.526, 'final': False}, {'from': 25.82, 'to': 26.15, 'speaker': 1, 'confidence': 0.526, 'final': False}, {'from': 26.18, 'to': 26.58, 'speaker': 1, 'confidence': 0.482, 'final': False}, {'from': 26.58, 'to': 26.97, 'speaker': 1, 'confidence': 0.482, 'final': False}, {'from': 27.0, 'to': 27.15, 'speaker': 1, 'confidence': 0.508, 'final': False}, {'from': 27.15, 'to': 27.37, 'speaker': 1, 'confidence': 0.508, 'final': False}, {'from': 27.53, 'to': 27.81, 'speaker': 1, 'confidence': 0.457, 'final': False}, {'from': 27.97, 'to': 28.73, 'speaker': 1, 'confidence': 0.466, 'final': False}, {'from': 28.85, 'to': 29.08, 'speaker': 1, 'confidence': 0.525, 'final': False}, {'from': 29.08, 'to': 29.54, 'speaker': 1, 'confidence': 0.525, 'final': False}, {'from': 29.54, 'to': 29.73, 'speaker': 1, 'confidence': 0.525, 'final': False}, {'from': 29.73, 'to': 30.04, 'speaker': 1, 'confidence': 0.525, 'final': False}, {'from': 30.04, 'to': 30.17, 'speaker': 1, 'confidence': 0.525, 'final': False}, {'from': 30.17, 'to': 30.47, 'speaker': 1, 'confidence': 0.525, 'final': False}, {'from': 31.31, 'to': 31.39, 'speaker': 1, 'confidence': 0.461, 'final': False}, {'from': 31.39, 'to': 31.77, 'speaker': 1, 'confidence': 0.461, 'final': False}, {'from': 31.77, 'to': 32.03, 'speaker': 1, 'confidence': 0.461, 'final': False}, {'from': 32.09, 'to': 32.93, 'speaker': 1, 'confidence': 0.616, 'final': False}, {'from': 33.19, 'to': 33.37, 'speaker': 1, 'confidence': 0.526, 'final': False}, {'from': 33.37, 'to': 33.81, 'speaker': 1, 'confidence': 0.526, 'final': False}, {'from': 33.88, 'to': 34.17, 'speaker': 1, 'confidence': 0.502, 'final': False}, {'from': 34.17, 'to': 34.52, 'speaker': 1, 'confidence': 0.502, 'final': False}, {'from': 34.52, 'to': 34.98, 'speaker': 1, 'confidence': 0.502, 'final': False}, {'from': 35.01, 'to': 35.46, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 35.46, 'to': 35.81, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 35.81, 'to': 35.97, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 35.97, 'to': 36.08, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 36.08, 'to': 36.17, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 36.17, 'to': 36.38, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 36.38, 'to': 36.51, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 36.51, 'to': 36.63, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 36.63, 'to': 36.87, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 36.87, 'to': 37.11, 'speaker': 1, 'confidence': 0.609, 'final': False}, {'from': 37.25, 'to': 37.86, 'speaker': 1, 'confidence': 0.466, 'final': False}, {'from': 37.89, 'to': 38.35, 'speaker': 1, 'confidence': 0.577, 'final': False}, {'from': 38.35, 'to': 38.73, 'speaker': 1, 'confidence': 0.577, 'final': False}, {'from': 39.13, 'to': 39.27, 'speaker': 1, 'confidence': 0.517, 'final': False}, {'from': 39.27, 'to': 39.49, 'speaker': 1, 'confidence': 0.517, 'final': False}, {'from': 39.8, 'to': 40.33, 'speaker': 1, 'confidence': 0.56, 'final': False}, {'from': 40.33, 'to': 40.47, 'speaker': 1, 'confidence': 0.56, 'final': False}, {'from': 40.47, 'to': 40.91, 'speaker': 1, 'confidence': 0.56, 'final': False}, {'from': 40.91, 'to': 41.05, 'speaker': 1, 'confidence': 0.56, 'final': False}, {'from': 41.05, 'to': 41.61, 'speaker': 1, 'confidence': 0.56, 'final': False}, {'from': 42.03, 'to': 42.39, 'speaker': 0, 'confidence': 0.542, 'final': False}, {'from': 42.39, 'to': 42.8, 'speaker': 0, 'confidence': 0.542, 'final': False}, {'from': 42.83, 'to': 43.2, 'speaker': 0, 'confidence': 0.441, 'final': False}, {'from': 43.26, 'to': 43.37, 'speaker': 0, 'confidence': 0.629, 'final': False}, {'from': 43.37, 'to': 43.85, 'speaker': 0, 'confidence': 0.629, 'final': False}, {'from': 43.88, 'to': 44.12, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 44.12, 'to': 44.24, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 44.24, 'to': 44.4, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 44.4, 'to': 44.63, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 44.63, 'to': 44.84, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 44.84, 'to': 45.26, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 45.26, 'to': 45.51, 'speaker': 0, 'confidence': 0.627, 'final': False}, {'from': 45.54, 'to': 46.17, 'speaker': 0, 'confidence': 0.585, 'final': False}, {'from': 46.17, 'to': 47.03, 'speaker': 0, 'confidence': 0.585, 'final': False}, {'from': 47.35, 'to': 47.62, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 47.62, 'to': 47.72, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 47.72, 'to': 47.92, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 47.92, 'to': 47.97, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 47.97, 'to': 48.09, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 48.09, 'to': 48.16, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 48.16, 'to': 48.26, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 48.26, 'to': 48.71, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 48.71, 'to': 48.77, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 48.77, 'to': 49.21, 'speaker': 0, 'confidence': 0.622, 'final': False}, {'from': 49.9, 'to': 50.04, 'speaker': 0, 'confidence': 0.5, 'final': False}, {'from': 50.04, 'to': 50.5, 'speaker': 0, 'confidence': 0.5, 'final': False}, {'from': 50.77, 'to': 51.21, 'speaker': 0, 'confidence': 0.532, 'final': False}, {'from': 51.8, 'to': 52.04, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.04, 'to': 52.25, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.25, 'to': 52.39, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.39, 'to': 52.51, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.51, 'to': 52.55, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.55, 'to': 52.7, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.7, 'to': 52.85, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 52.85, 'to': 53.08, 'speaker': 0, 'confidence': 0.502, 'final': False}, {'from': 53.44, 'to': 53.95, 'speaker': 0, 'confidence': 0.576, 'final': False}, {'from': 53.98, 'to': 54.16, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 54.16, 'to': 54.56, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 54.56, 'to': 54.68, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 54.68, 'to': 54.82, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 54.82, 'to': 55.4, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 55.4, 'to': 55.7, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 55.7, 'to': 55.96, 'speaker': 0, 'confidence': 0.636, 'final': False}, {'from': 56.21, 'to': 56.39, 'speaker': 0, 'confidence': 0.639, 'final': False}, {'from': 56.39, 'to': 56.5, 'speaker': 0, 'confidence': 0.639, 'final': False}, {'from': 56.5, 'to': 56.92, 'speaker': 0, 'confidence': 0.639, 'final': False}, {'from': 56.92, 'to': 57.03, 'speaker': 0, 'confidence': 0.639, 'final': False}, {'from': 57.03, 'to': 57.38, 'speaker': 0, 'confidence': 0.639, 'final': False}, {'from': 57.67, 'to': 57.87, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 57.87, 'to': 58.39, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 58.39, 'to': 58.63, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 58.63, 'to': 58.77, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 58.77, 'to': 59.2, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 59.2, 'to': 59.3, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 59.3, 'to': 59.53, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 59.53, 'to': 59.88, 'speaker': 0, 'confidence': 0.631, 'final': False}, {'from': 60.05, 'to': 60.35, 'speaker': 0, 'confidence': 0.581, 'final': False}, {'from': 60.35, 'to': 60.47, 'speaker': 0, 'confidence': 0.581, 'final': False}, {'from': 60.47, 'to': 60.85, 'speaker': 0, 'confidence': 0.581, 'final': False}, {'from': 60.85, 'to': 61.01, 'speaker': 0, 'confidence': 0.581, 'final': False}, {'from': 61.01, 'to': 61.15, 'speaker': 0, 'confidence': 0.581, 'final': False}, {'from': 61.15, 'to': 61.87, 'speaker': 0, 'confidence': 0.581, 'final': True}]}

I am looking to transform it into a usable format and struggling.

The ideal format would be grouping and combining the timestamped words by speaker, and listing the speaker label, the minimum from, and the maximum to. Creating a list of speaker sentences. Similar to below:

Table example

Any help would be appreciated.

Answer 1

You could use this json and create a Pandas dataframe. It has methods for grouping, aggregating and sorting data as per your needs.

Answer 2

As answered by Kulkarni, you'll have to use panda dataframe to achieve the desired outcome. You can refer to the sample code below. For your context, I used the notebook on watson studio to achieve this by importing your json and using panda to give it the desired data structure.

 import json
    import pandas as pd
    from botocore.client import Config
    import ibm_boto3


    # @hidden_cell
    # The following code contains the credentials for a file in your IBM Cloud Object Storage.
    # You might want to remove those credentials before you share your notebook.
    credentials_1 = {
        'IBM_API_KEY_ID': 'AsauselesssajHY_zsdfQxDc-gsss8M-gxcxw_2asd',
        'IAM_SERVICE_ID': 'iam-ServiceId-fenotnf6-3dsd-4125-3116-5f131119d',
        'ENDPOINT': 'https://s3-api.us-geo.objectstorage.service.networklayer.com',
        'IBM_AUTH_ENDPOINT': 'https://iam.ng.bluemix.net/oidc/token',
        'BUCKET': 'asd1231sdsd-donotdelete-pr-4ecvinl7uiurbu',
        'FILE': 'samplejson02.json'
    }
sampleTextFileName = "samplejson02.json"

cos = ibm_boto3.client('s3',
                    ibm_api_key_id=credentials_1['IBM_API_KEY_ID'],
                    ibm_service_instance_id=credentials_1['IAM_SERVICE_ID'],
                    ibm_auth_endpoint=credentials_1['IBM_AUTH_ENDPOINT'],
                    config=Config(signature_version='oauth'),
                    endpoint_url=credentials_1['ENDPOINT'])

def get_file(filename):
    fileobject = cos.get_object(Bucket=credentials_1['BUCKET'], Key=filename)['Body']
    return fileobject

def load_string(fileobject):
    text = fileobject.read()
    return text.decode('utf-8')

Ingest the following part into your code.

sampletext = load_string(get_file(sampleTextFileName))
jsontext = json.loads(sampletext);
df=pd.DataFrame(jsontext['results'][0]['alternatives'][0]['timestamps'])
print(df)

The expected outcome is:

cross_reference_image

To get the nested result you want, try the following bit of code.

df1 = pd.DataFrame()
for result in jsontext['results']:
    for alternative in result['alternatives']:
        for timestamp in alternative['timestamps']:
            for speaker_label in jsontext['speaker_labels']:
                if(timestamp[1]==speaker_label.get('from') and timestamp[2]==speaker_label.get('to')):
                    frame = [timestamp[0],timestamp[1],timestamp[2],speaker_label.get('speaker')]
                    df=pd.DataFrame(frame)
                    df1=df1.append(df.transpose())
print(df1)

The logic that I have put together traverses down each label and then, for every matching "from" and "to", it creates a new frame that'll be concatenated in an an already initialized dataframe object "df1".

The expected outcome will be:

cross_reference_image_02

GL!