Update app.py

app.py

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+from transformers import pipeline, AutoTokenizer, T5ForConditionalGeneration
+
+
+model_name = "IlyaGusev/rut5_base_sum_gazeta"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model_sum = T5ForConditionalGeneration.from_pretrained(model_name)
+
+
+def summ_mT5_G(text):
+    input_ids = tokenizer(
+        [text],
+        max_length=600,
+        add_special_tokens=True,
+        padding="max_length",
+        truncation=True,
+        return_tensors="pt"
+    )["input_ids"]
+    output_ids = model_sum.generate(
+        input_ids=input_ids,
+        no_repeat_ngram_size=4
+    )[0]
+
+    summary = tokenizer.decode(output_ids, skip_special_tokens=True)
+    return summary
+
+import torch
+
+
+# punctuation
+model_punc, example_texts, languages, punct, apply_te = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_te')
+
+def punct(text):
+    # print(text)
+    return apply_te(text.lower(), lan='ru')
+
+
+from pyannote.audio import Pipeline
+import os
+
+
+pipeline_a = Pipeline.from_pretrained(
+    "pyannote/speaker-diarization-3.1",
+    use_auth_token=str(os.getenv(s1)))
+
+
+# верификация
+def speackers_list(audio_f : str):
+    # # send pipeline to GPU (when available)
+    # import torch
+    # pipeline.to(torch.device("cuda"))
+
+    # apply pretrained pipeline
+    diarization = pipeline_a(audio_f)
+    speackers_list = []
+    # print the result
+    for turn, _, speaker in diarization.itertracks(yield_label=True):
+        # print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
+        speackers_list.append([turn.start, turn.end, speaker])
+
+    # speackers_list.sort(key = lambda x: x[0])
+    # print(speackers_list)
+    i = 0
+    while i<len(speackers_list)-1:
+        if speackers_list[i][-1] == speackers_list[i+1][-1] and (i==0 or (speackers_list[i-1][1]<speackers_list[i][1])):
+              speackers_list[i][1] = speackers_list[i+1][1]
+              speackers_list.pop(i+1)
+        else:
+          i+=1
+    return speackers_list
+
+
+# speackers = speackers_list(name_of_file)
+
+
+
+from faster_whisper import WhisperModel
+
+
+model_size = "large-v3"
+# Run on GPU with FP16
+model_tts = WhisperModel(model_size)  #, lan = "ru") #,  device="cpu", compute_type="int8") #, device="cuda", compute_type="float16")
+
+def speach_to_text(file_name):
+
+    segments, info = model_tts.transcribe(file_name, beam_size=5)
+
+    # print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
+    text_of_seg = ""
+    for segment in segments:
+        # segment_text = Segment_text(text = segment.text, start = round(segment.start,5), end = round(segment.end, 5))
+        # list_of_segments.append(segment_text)
+        # print(f"[{segment.start:.2f}  -> {segment.end:.2f}] {segment.text}")
+
+        text_of_seg += segment.text
+    # return list_of_segments
+    return text_of_seg
+
+
+class Segment_text:
+    text : str # текст сегмента, примененный
+    start : float # время начала сегмента
+    end : float  # время окончания сегмента
+    speacker : str # верифицированный спикер
+    summarization : str # абстракт исходного текста сегмента
+
+
+    def __init__(self, text : str, start : float, end : float, speacker : str ): # , summarization : str):
+        self.text = punct(text)
+        self.start = start
+        self.end = end
+        self.speacker = speacker
+        self.value = len(text)
+        self.summarization = summ_mT5_G(text) if self.value > 200 else text # summarization
+
+    def get_text(self):
+        return self.text
+
+    def get_time(self):
+        return (self.start, self.end)
+
+    def get_summarization(self):
+        return self.summarization
+
+    def get_speacker(self):
+        return self.speacker
+
+from pydub import AudioSegment
+def init_segments(speackers, name_of_file):
+    list_of_segments = []
+    audio = AudioSegment.from_file(name_of_file)
+    for ind, seg in enumerate(speackers):
+        temp_seg = audio[seg[0]*10**3 : seg[1]*10**3]
+        name_of_seg = "seg"+str(ind)+".mp3"
+        temp_seg.export(name_of_seg, format="mp3")
+
+        temp_text = speach_to_text(name_of_seg)
+        segment_text = Segment_text(text = temp_text, start = seg[0], end = seg[1], speacker = seg[2])
+        list_of_segments.append(segment_text)
+    return list_of_segments
+
+
+def get_text_to_out(list_of_segments : list):
+    res_text, res_sum = "", ""
+    for seg in list_of_segments:
+        res_text += f"{seg.get_speacker()} : {seg.get_text()}\n"
+    for seg in list_of_segments:
+        res_sum += seg.get_speacker() + ":  " + seg.get_summarization() + "\n"
+    return res_text, res_sum
+
+from random import randint
+
+
+def do_smth(file):
+    audio = AudioSegment.from_wav(file)
+    name_of_file = "f"+str(randint(1,10**8))
+    audio.export(name_of_file, format="mp3")
+
+
+    speackers = speackers_list(name_of_file)
+
+    list_of_segments = init_segments(speackers, name_of_file)
+
+    out_text, out_sum = get_text_to_out(list_of_segments)
+
+    return out_text, out_sum
+
+import gradio as gr
+
+
+demo = gr.Interface(
+    do_smth,
+    gr.Audio(type="filepath"),
+    [
+        gr.Textbox(value=1, label="Исходный текст"),
+        gr.Textbox(value=1, label="Сокращенный текст")
+    ]
+)
+demo.launch()