diff --git a/TTS/bin/compute_embeddings.py b/TTS/bin/compute_embeddings.py
index 689f1c58..64edd140 100644
--- a/TTS/bin/compute_embeddings.py
+++ b/TTS/bin/compute_embeddings.py
@@ -9,7 +9,7 @@ import torch
 from TTS.speaker_encoder.model import SpeakerEncoder
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_config
-from TTS.utils.io import save_speaker_mapping
+from TTS.tts.utils.speakers import save_speaker_mapping
 from TTS.tts.datasets.preprocess import load_meta_data
 
 parser = argparse.ArgumentParser(
@@ -108,21 +108,23 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
     if isinstance(wav_file, list):
         speaker_name = wav_file[2]
         wav_file = wav_file[1]
+
     mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T
     mel_spec = torch.FloatTensor(mel_spec[None, :, :])
     if args.use_cuda:
         mel_spec = mel_spec.cuda()
     embedd = model.compute_embedding(mel_spec)
-    np.save(output_files[idx], embedd.detach().cpu().numpy())
+    embedd = embedd.detach().cpu().numpy()
+    np.save(output_files[idx], embedd)
 
     if args.target_dataset != '':
         # create speaker_mapping if target dataset is defined
         wav_file_name = os.path.basename(wav_file)
         speaker_mapping[wav_file_name] = {}
         speaker_mapping[wav_file_name]['name'] = speaker_name
-        speaker_mapping[wav_file_name]['embedding'] = embedd.detach().cpu().numpy()
+        speaker_mapping[wav_file_name]['embedding'] = embedd.flatten().tolist()
 
 if args.target_dataset != '':
     # save speaker_mapping if target dataset is defined
     mapping_file_path = os.path.join(args.output_path, 'speakers.json')
-    save_speaker_mapping(mapping_file_path, speaker_mapping)
+    save_speaker_mapping(args.output_path, speaker_mapping)