Update

app.py

@@ -16,135 +16,6 @@ import torchaudio
 from funasr import AutoModel
 from sv import clean_and_emoji_annotate_speech, process_audio
 
-model = "FunAudioLLM/SenseVoiceSmall"
-model = AutoModel(
-    model=model,
-    vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
-    vad_kwargs={"max_single_segment_time": 30000},
-    hub="hf",
-    device="cuda",
-)
-
-import re
-
-emo_dict = {
-    "<|HAPPY|>": "😊",
-    "<|SAD|>": "😔",
-    "<|ANGRY|>": "😡",
-    "<|NEUTRAL|>": "",
-    "<|FEARFUL|>": "😰",
-    "<|DISGUSTED|>": "🤢",
-    "<|SURPRISED|>": "😮",
-}
-
-event_dict = {
-    "<|BGM|>": "🎼",
-    "<|Speech|>": "",
-    "<|Applause|>": "👏",
-    "<|Laughter|>": "😀",
-    "<|Cry|>": "😭",
-    "<|Sneeze|>": "🤧",
-    "<|Breath|>": "",
-    "<|Cough|>": "🤧",
-}
-
-emoji_dict = {
-    "<|nospeech|><|Event_UNK|>": "❓",
-    "<|zh|>": "",
-    "<|en|>": "",
-    "<|yue|>": "",
-    "<|ja|>": "",
-    "<|ko|>": "",
-    "<|nospeech|>": "",
-    "<|HAPPY|>": "😊",
-    "<|SAD|>": "😔",
-    "<|ANGRY|>": "😡",
-    "<|NEUTRAL|>": "",
-    "<|BGM|>": "🎼",
-    "<|Speech|>": "",
-    "<|Applause|>": "👏",
-    "<|Laughter|>": "😀",
-    "<|FEARFUL|>": "😰",
-    "<|DISGUSTED|>": "🤢",
-    "<|SURPRISED|>": "😮",
-    "<|Cry|>": "😭",
-    "<|EMO_UNKNOWN|>": "",
-    "<|Sneeze|>": "🤧",
-    "<|Breath|>": "",
-    "<|Cough|>": "😷",
-    "<|Sing|>": "",
-    "<|Speech_Noise|>": "",
-    "<|withitn|>": "",
-    "<|woitn|>": "",
-    "<|GBG|>": "",
-    "<|Event_UNK|>": "",
-}
-
-lang_dict = {
-    "<|zh|>": "<|lang|>",
-    "<|en|>": "<|lang|>",
-    "<|yue|>": "<|lang|>",
-    "<|ja|>": "<|lang|>",
-    "<|ko|>": "<|lang|>",
-    "<|nospeech|>": "<|lang|>",
-}
-
-emo_set = {"😊", "😔", "😡", "😰", "🤢", "😮"}
-event_set = {"🎼", "👏", "😀", "😭", "🤧", "😷"}
-
-
-def format_str(s):
-    for sptk in emoji_dict:
-        s = s.replace(sptk, emoji_dict[sptk])
-    return s
-
-
-def format_str_v2(s):
-    sptk_dict = {}
-    for sptk in emoji_dict:
-        sptk_dict[sptk] = s.count(sptk)
-        s = s.replace(sptk, "")
-    emo = "<|NEUTRAL|>"
-    for e in emo_dict:
-        if sptk_dict[e] > sptk_dict[emo]:
-            emo = e
-    for e in event_dict:
-        if sptk_dict[e] > 0:
-            s = event_dict[e] + s
-    s = s + emo_dict[emo]
-
-    for emoji in emo_set.union(event_set):
-        s = s.replace(" " + emoji, emoji)
-        s = s.replace(emoji + " ", emoji)
-    return s.strip()
-
-
-def format_str_v3(s):
-    def get_emo(s):
-        return s[-1] if s[-1] in emo_set else None
-
-    def get_event(s):
-        return s[0] if s[0] in event_set else None
-
-    s = s.replace("<|nospeech|><|Event_UNK|>", "❓")
-    for lang in lang_dict:
-        s = s.replace(lang, "<|lang|>")
-    s_list = [format_str_v2(s_i).strip(" ") for s_i in s.split("<|lang|>")]
-    new_s = " " + s_list[0]
-    cur_ent_event = get_event(new_s)
-    for i in range(1, len(s_list)):
-        if len(s_list[i]) == 0:
-            continue
-        if get_event(s_list[i]) == cur_ent_event and get_event(s_list[i]) != None:
-            s_list[i] = s_list[i][1:]
-        # else:
-        cur_ent_event = get_event(s_list[i])
-        if get_emo(s_list[i]) != None and get_emo(s_list[i]) == get_emo(new_s):
-            new_s = new_s[:-1]
-        new_s += s_list[i].strip().lstrip()
-    new_s = new_s.replace("The.", " ")
-    return new_s.strip()
-
 
 @spaces.GPU
 def model_inference(input_wav, language, fs=16000):