Update libriheavy.py

libriheavy.py

@@ -54,6 +54,7 @@ class Libriheavy(datasets.GeneratorBasedBuilder):
                 {
                     "id": datasets.Value("string"),
                     "speaker_id": datasets.Value("string"),
+                    "speaker_vec": datasets.Sequence(datasets.Value("float32")),
                     "audio": datasets.Value("string"),
                     "text": datasets.Value("string"),
                     "word_segments": datasets.Sequence(
@@ -71,6 +72,22 @@ class Libriheavy(datasets.GeneratorBasedBuilder):
                         }
                     ),
                     "mel_spectrogram": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                    "attributes": datasets.Sequence(
+                        {
+                            "pitch": datasets.Sequence(datasets.Value("float32")),
+                            "energy": datasets.Sequence(datasets.Value("float32")),
+                            "snr": datasets.Sequence(datasets.Value("float32")),
+                            "srmr": datasets.Sequence(datasets.Value("float32")),
+                        }
+                    ),
+                    "overall_attributes": datasets.Sequence(
+                        {
+                            "pitch": datasets.Value("float32"),
+                            "energy": datasets.Value("float32"),
+                            "snr": datasets.Value("float32"),
+                            "srmr": datasets.Value("float32"),
+                        }
+                    ),
                 }
             ),
             supervised_keys=None,
@@ -153,10 +170,11 @@ class Libriheavy(datasets.GeneratorBasedBuilder):
                     # skip the last utterance
                     if utterance_id == sorted(list(text.keys()))[-1]:
                         continue
-                    print(npz[str(utterance_id)].item().keys())
+                    npz_item = npz[str(utterance_id)].item()
                     result =  {
                         "id": chunk["speaker_id"] + "_" + utterance_id,
                         "speaker_id": chunk["speaker_id"],
+                        "speaker_vec": npz_item["d_vector"],
                         "audio": chunk["audio"],
                         "text": chunk["text"],
                         "word_segments": [
@@ -165,24 +183,50 @@ class Libriheavy(datasets.GeneratorBasedBuilder):
                         "phone_segments": [
                             {"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"]
                         ],
-                        "mel_spectrogram": npz[str(utterance_id)].item()["mel"][0][0],
+                        "mel_spectrogram": npz_item["mel"][0][0],
+                        "attributes": {
+                            npz_item["pitch"],
+                            npz_item["energy"],
+                            npz_item["snr"],
+                            npz_item["srmr"],
+                        },
+                        "overall_attributes": {
+                            npz_item["overall_pitch"],
+                            npz_item["overall_energy"],
+                            npz_item["overall_snr"],
+                            npz_item["overall_srmr"],
+                        },
                     }
                     yield chunk["speaker_id"] + "_" + utterance_id, result
             else:
                 # only use the last utterance
                 utterance_id = sorted(list(text.keys()))[-1]
                 utterance = text[utterance_id]
-                result =  {
-                    "id": chunk["speaker_id"] + "_" + utterance_id,
-                    "speaker_id": chunk["speaker_id"],
-                    "audio": chunk["audio"],
-                    "text": chunk["text"],
-                    "word_segments": [
-                        {"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"]
-                    ],
-                    "phone_segments": [
-                        {"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"]
-                    ],
-                    "mel_spectrogram": npz[str(utterance_id)].item()["mel"][0][0],
-                }
+                npz_item = npz[str(utterance_id)].item()
+                    result =  {
+                        "id": chunk["speaker_id"] + "_" + utterance_id,
+                        "speaker_id": chunk["speaker_id"],
+                        "speaker_vec": npz_item["d_vector"],
+                        "audio": chunk["audio"],
+                        "text": chunk["text"],
+                        "word_segments": [
+                            {"start": segment[0], "end": segment[1], "word": segment[2]} for segment in utterance["word_segments"]
+                        ],
+                        "phone_segments": [
+                            {"start": segment[0], "end": segment[1], "phone": segment[2]} for segment in utterance["phone_segments"]
+                        ],
+                        "mel_spectrogram": npz_item["mel"][0][0],
+                        "attributes": {
+                            npz_item["pitch"],
+                            npz_item["energy"],
+                            npz_item["snr"],
+                            npz_item["srmr"],
+                        },
+                        "overall_attributes": {
+                            npz_item["overall_pitch"],
+                            npz_item["overall_energy"],
+                            npz_item["overall_snr"],
+                            npz_item["overall_srmr"],
+                        },
+                    }
                 yield chunk["speaker_id"] + "_" + utterance_id, result