Remove old unused utils

utils_vad.py

@@ -379,72 +379,6 @@ def get_speech_timestamps(audio: torch.Tensor,
     return speeches
 
 
-def get_number_ts(wav: torch.Tensor,
-                  model,
-                  model_stride=8,
-                  hop_length=160,
-                  sample_rate=16000):
-    wav = torch.unsqueeze(wav, dim=0)
-    perframe_logits = model(wav)[0]
-    perframe_preds = torch.argmax(torch.softmax(perframe_logits, dim=1), dim=1).squeeze()   # (1, num_frames_strided)
-    extended_preds = []
-    for i in perframe_preds:
-        extended_preds.extend([i.item()] * model_stride)
-    # len(extended_preds) is *num_frames_real*; for each frame of audio we know if it has a number in it.
-    triggered = False
-    timings = []
-    cur_timing = {}
-    for i, pred in enumerate(extended_preds):
-        if pred == 1:
-            if not triggered:
-                cur_timing['start'] = int((i * hop_length) / (sample_rate / 1000))
-                triggered = True
-        elif pred == 0:
-            if triggered:
-                cur_timing['end'] = int((i * hop_length) / (sample_rate / 1000))
-                timings.append(cur_timing)
-                cur_timing = {}
-                triggered = False
-    if cur_timing:
-        cur_timing['end'] = int(len(wav) / (sample_rate / 1000))
-        timings.append(cur_timing)
-    return timings
-
-
-def get_language(wav: torch.Tensor,
-                 model):
-    wav = torch.unsqueeze(wav, dim=0)
-    lang_logits = model(wav)[2]
-    lang_pred = torch.argmax(torch.softmax(lang_logits, dim=1), dim=1).item()   # from 0 to len(languages) - 1
-    assert lang_pred < len(languages)
-    return languages[lang_pred]
-
-
-def get_language_and_group(wav: torch.Tensor,
-                           model,
-                           lang_dict: dict,
-                           lang_group_dict: dict,
-                           top_n=1):
-    wav = torch.unsqueeze(wav, dim=0)
-    lang_logits, lang_group_logits = model(wav)
-
-    softm = torch.softmax(lang_logits, dim=1).squeeze()
-    softm_group = torch.softmax(lang_group_logits, dim=1).squeeze()
-
-    srtd = torch.argsort(softm, descending=True)
-    srtd_group = torch.argsort(softm_group, descending=True)
-
-    outs = []
-    outs_group = []
-    for i in range(top_n):
-        prob = round(softm[srtd[i]].item(), 2)
-        prob_group = round(softm_group[srtd_group[i]].item(), 2)
-        outs.append((lang_dict[str(srtd[i].item())], prob))
-        outs_group.append((lang_group_dict[str(srtd_group[i].item())], prob_group))
-
-    return outs, outs_group
-
-
 class VADIterator:
     def __init__(self,
                  model,