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camparchimedes
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64e12f4
1
Parent(s):
dda0718
Update app.py
Browse files
app.py
CHANGED
@@ -23,7 +23,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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torch_dtype = torch.float32
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# ASR pipeline
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-
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# Switch m4a to wav
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def convert_to_wav(audio_file):
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@@ -35,7 +35,7 @@ def convert_to_wav(audio_file):
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# @spaces.GPU(queue=True)
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# Transcription funct.@ASR
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def transcribe_audio(audio_file):
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if audio_file.endswith(".m4a"):
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audio_file = convert_to_wav(audio_file)
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@@ -43,7 +43,7 @@ def transcribe_audio(audio_file):
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start_time = time.time()
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with torch.no_grad():
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output =
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transcription = output["text"]
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end_time = time.time()
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@@ -82,7 +82,7 @@ def summarize_text(text):
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# HTML syntax for imagery
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<div style="text-align: center;">
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<img src="https://huggingface.co/spaces/camparchimedes/ola_s-audioshop/raw/main/picture.png" alt="Banner" width="85%" height="auto">
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</div>
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@@ -107,7 +107,7 @@ def save_to_pdf(transcription, summary):
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pdf.output(pdf_output_path)
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return pdf_output_path
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# Gradio
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iface = gr.Interface(
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fn=transcribe_audio,
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inputs=gr.Audio(type="filepath"),
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@@ -120,7 +120,7 @@ iface = gr.Interface(
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iface = gr.Blocks()
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with iface:
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gr.HTML(
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gr.Markdown("# Vi har nå muligheten til å oversette lydfiler til norsk skrift.")
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with gr.Tabs():
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@@ -197,4 +197,4 @@ with iface:
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# run
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iface.launch(share=True, debug=True)
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torch_dtype = torch.float32
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# ASR pipeline
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asr = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large-semantic", device=device, torch_dtype=torch_dtype)
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# Switch m4a to wav
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def convert_to_wav(audio_file):
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# @spaces.GPU(queue=True)
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# Transcription funct.@ASR
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def transcribe_audio(audio_file):
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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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with torch.no_grad():
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output = asr(audio_file, chunk_length_s=30, generate_kwargs={"num_beams": 8, "task": "transcribe", "language": "no"})
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transcription = output["text"]
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end_time = time.time()
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# HTML syntax for imagery
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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/ola_s-audioshop/raw/main/picture.png" alt="Banner" width="85%" height="auto">
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</div>
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pdf.output(pdf_output_path)
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return pdf_output_path
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# Gradio
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iface = gr.Interface(
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fn=transcribe_audio,
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inputs=gr.Audio(type="filepath"),
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iface = gr.Blocks()
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with iface:
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gr.HTML(banner_html)
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gr.Markdown("# Vi har nå muligheten til å oversette lydfiler til norsk skrift.")
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with gr.Tabs():
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# run
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iface.launch(share=True, debug=True)
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