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Update app.py
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app.py
CHANGED
@@ -21,7 +21,7 @@ def convert_to_wav(audio_file):
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return wav_file
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import torch
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from transformers import pipeline #
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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torch_dtype = torch.float32
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@@ -29,17 +29,22 @@ torch_dtype = torch.float32
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pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large", device=device, torch_dtype=torch_dtype)
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# @spaces.GPU(queue=True)
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def transcribe_audio(audio_file, forced_decoder_ids):
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if audio_file.endswith(".m4a"):
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audio_file = convert_to_wav(audio_file)
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start_time = time.time()
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forced_decoder_ids = processor.get_decoder_prompt_ids(language=
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# check if still the case...........??*********************************************
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# "You have passed task=transcribe, but also have set `forced_decoder_ids` to [[1, 50288], [2, 50360], [3, 50364]] which creates a conflict. `forced_decoder_ids` will be ignored in favor of task=transcribe."
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with torch.no_grad():
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output = pipe(audio_file, chunk_length_s=30, generate_kwargs={"forced_decoder_ids
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text = output["text"]
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end_time = time.time()
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@@ -50,6 +55,7 @@ def transcribe_audio(audio_file, forced_decoder_ids):
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return text, result
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# [VERSION 3: full-on w/ 3 styles for summarization]
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import nltk
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from nltk.tokenize import word_tokenize, sent_tokenize
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@@ -199,7 +205,7 @@ def save_to_pdf(text, summary):
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banner_html = """
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<div style="text-align: center;">
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<img src="https://huggingface.co/spaces/camparchimedes/
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</div>
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"""
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return wav_file
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import torch
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from transformers import pipeline, AutoProcessor # AutoModelForSpeechSeq2Seq
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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torch_dtype = torch.float32
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pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large", device=device, torch_dtype=torch_dtype)
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# @spaces.GPU(queue=True)
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# Initialize processor before using it in the function
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processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-large")
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language = "no"
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task = "transcribe"
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def transcribe_audio(audio_file, forced_decoder_ids):
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if audio_file.endswith(".m4a"):
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audio_file = convert_to_wav(audio_file)
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start_time = time.time()
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forced_decoder_ids = processor.get_decoder_prompt_ids(language="no", task="transcribe")
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with torch.no_grad():
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output = pipe(audio_file, chunk_length_s=30, generate_kwargs={"forced_decoder_ids": forced_decoder_ids})
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text = output["text"]
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end_time = time.time()
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return text, result
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# [VERSION 3: full-on w/ 3 styles for summarization]
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import nltk
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from nltk.tokenize import word_tokenize, sent_tokenize
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banner_html = """
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<div style="text-align: center;">
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<img src="https://huggingface.co/spaces/camparchimedes/transcription_app/blob/main/Olas%20AudioSwitch%20Shop.png" alt="Banner" width="100%" height="auto">
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</div>
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"""
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