added sentiment analysis
This commit is contained in:
SayanoAI
+46
-23
@@ -20,8 +20,17 @@ from textacy.extract import keyterms as kt
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temp_path = folder_paths.get_temp_directory()
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CATEGORY = "🌺RVC-Studio/stt"
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def extract_keywords(text: str, max_words: int, spacy_model, prefix="", suffix="", **kwargs):
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def extract_keywords(text: str, max_words: int, spacy_model, use_sentiment=False, prefix="", suffix="", **kwargs):
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text = text.strip()
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sentiment = ""
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if use_sentiment:
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from spacytextblob.spacytextblob import SpacyTextBlob
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doc = spacy_model(text)
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polarity = SpacyTextBlob(spacy_model).get_polarity(doc)
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if polarity<-.5: sentiment="sad, tears"
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elif polarity<-.2: sentiment="sad"
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elif polarity>.5: sentiment="happy, smile"
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elif polarity>.2: sentiment="slight smile"
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doc = textacy.make_spacy_doc(text, lang=spacy_model)
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topn = int(max_words) if max_words>0 else len(text)
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include_pos = ["NOUN","ADJ","PROPN","VERB","NUM","ADP"]
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@@ -33,14 +42,23 @@ def extract_keywords(text: str, max_words: int, spacy_model, prefix="", suffix="
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except Exception as error:
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print(f"{text=} {error=}")
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return ", ".join(filter(None,[prefix,*tags,suffix])).strip()
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return ", ".join(filter(None,[prefix,*tags,sentiment,suffix])).strip()
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def limit_sentence(text: str, max_words: int, prefix="", suffix="", **kwargs):
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def limit_sentence(text: str, max_words: int, spacy_model, use_sentiment=False, prefix="", suffix="", **kwargs):
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text = text.strip()
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sentiment = ""
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if use_sentiment:
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from spacytextblob.spacytextblob import SpacyTextBlob
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doc = spacy_model(text)
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polarity = SpacyTextBlob(spacy_model).get_polarity(doc)
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if polarity<-.5: sentiment="sad, tears"
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elif polarity<-.2: sentiment="sad"
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elif polarity>.5: sentiment="happy, smile"
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elif polarity>.2: sentiment="slight smile"
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topn = int(max_words) if max_words>0 else len(text)
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if topn>0: text = " ".join(text.split()[:topn])
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return ", ".join(filter(None,[prefix,text,suffix])).strip()
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return ", ".join(filter(None,[prefix,text,sentiment,suffix])).strip()
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class AudioTranscriptionNode:
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def __init__(self):
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@@ -135,13 +153,14 @@ class BatchedTranscriptionEncoderNode:
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return {
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"required": {
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"transcription": ('TRANSCRIPTION',),
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"num_frames": ('INT', {'forceInput': True,}),
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"clip": ('CLIP',)
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},
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"optional": {
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"loop": ('BOOLEAN', {'default': False}),
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"use_tags": ('BOOLEAN', {'default': False}),
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"use_sentiment": ('BOOLEAN', {'default': False}),
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'language': (SUPPORTED_LANGUAGES,{"default": "en"}),
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"max_chunks": ('INT', {"min": 2, "default": None}),
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"max_words": ('INT', {'default': 16, "min": 0, "max": 32, "display": "slider"}),
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"frame_interpolation": ("INT", {"default": 0, "min": 0, "max": 120}),
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"prefix": ("STRING", {"default": "masterpiece, best quality", "multiline": True, "forceInput": True}),
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@@ -159,16 +178,14 @@ class BatchedTranscriptionEncoderNode:
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CATEGORY = CATEGORY
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@staticmethod
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def process_text_chunks(ichunk,text_processor,frame_interpolation,clip,**kwargs):
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def process_text_chunks(ichunk,frame_interpolation,clip,*,use_tags,**kwargs):
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i,chunk=ichunk
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frame_interpolation=int(frame_interpolation)
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text_processor = extract_keywords if use_tags else limit_sentence
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text = text_processor(chunk["text"],**kwargs)
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timestamp = np.nan_to_num(np.array(chunk["timestamp"],dtype=float),nan=i)
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# index = int(timestamp[0]*frame_interpolation) if frame_interpolation>0 else i
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# prompt = f'"{index}":"{text}"' if len(text) else f'"{index}":""'
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duration = max(abs(int(np.round(timestamp[1]-timestamp[0]))),1) # at least 1 sec duration
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if frame_interpolation>1: duration*=frame_interpolation
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# value = f'"{index}":"({duration})"'
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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@@ -192,15 +209,18 @@ class BatchedTranscriptionEncoderNode:
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model = spacy.load(model_name)
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model.to_disk(model_path)
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return spacy.load(model_path)
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model = spacy.load(model_path)
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return model
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def get_prompt(
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self, transcription, num_frames, clip, language="en", loop=False, use_tags=False, max_words=16,
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frame_interpolation=0, print_output=True, prefix="", suffix=""
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self, transcription, clip, language="en", loop=False, use_tags=False, use_sentiment=False,
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max_words=16,max_chunks=None, frame_interpolation=0, print_output=True, prefix="", suffix=""
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):
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print('Transcription Done')
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total_chunks = transcription['chunks']
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if not max_chunks: max_chunks = len(transcription['chunks'])
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total_chunks = transcription['chunks'][:max_chunks]
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num_frames = max(max_chunks, *filter(None,np.array([chunk["timestamp"] for chunk in transcription['chunks']]).flatten()))
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if loop: # append first frame to chunk stack
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last_chunk = None
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@@ -210,10 +230,11 @@ class BatchedTranscriptionEncoderNode:
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if "timestamp" in last_chunk and len(last_chunk["timestamp"])>=1:
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last_chunk["timestamp"] = np.nan_to_num(np.array(last_chunk["timestamp"],dtype=float),nan=num_frames)
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break
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if last_chunk is not None:
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timestamp = last_chunk["timestamp"]
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start_time = timestamp[-1]
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duration = max(1,num_frames - start_time)
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duration = max(abs(num_frames - start_time),1)
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total_chunks.append({
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"text": total_chunks[0]["text"],
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"timestamp": (start_time,start_time+duration)
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@@ -221,10 +242,7 @@ class BatchedTranscriptionEncoderNode:
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else: # adds 1s frame to prevent audio from stopping early
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num_frames+=1
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if use_tags:
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spacy_model,text_processor = self.get_spacy_model(language),extract_keywords
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else:
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spacy_model,text_processor = None,limit_sentence
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spacy_model = self.get_spacy_model(language)
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# split transcript into prompt based on timestamp
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# find length of each frame using timestamp
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@@ -233,7 +251,13 @@ class BatchedTranscriptionEncoderNode:
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cond = []
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pooled = []
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for ichunk in enumerate(total_chunks):
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t, d, c, pc = self.process_text_chunks(ichunk,text_processor,frame_interpolation,clip,prefix=prefix,suffix=suffix,spacy_model=spacy_model,max_words=max_words)
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t, d, c, pc = self.process_text_chunks(ichunk,frame_interpolation,clip,
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use_tags=use_tags,
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use_sentiment=use_sentiment,
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prefix=prefix,
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suffix=suffix,
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spacy_model=spacy_model,
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max_words=max_words)
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batch_prompt_text.append(t)
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batch_values_int.append(d)
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cond.append(c)
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@@ -246,10 +270,9 @@ class BatchedTranscriptionEncoderNode:
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del pooled, cond
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if print_output:
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pprint(conditioning)
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print(batch_prompt_text)
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print(batch_values_int)
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print(f"{num_chunks=}, {num_frames=}")
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print(f"{num_chunks=}, {max_chunks=}, {num_frames=}")
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return (conditioning, batch_prompt_text, batch_values_int, num_chunks, num_frames)
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+2
-1
@@ -13,4 +13,5 @@ pyaudio
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spacy
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monotonic_align
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textacy
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pytube
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pytube
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spacytextblob
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