Update app.py
Browse files
app.py
CHANGED
@@ -1,14 +1,15 @@
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import gradio as gr
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#import torch
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import whisper
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from datetime import datetime
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from PIL import Image
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import flag
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import os
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#MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
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#from diffusers import StableDiffusionPipeline
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stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
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### ββββββββββββββββββββββββββββββββββββββββ
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@@ -16,7 +17,7 @@ title="Whisper to Stable Diffusion"
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### ββββββββββββββββββββββββββββββββββββββββ
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whisper_model = whisper.load_model("small")
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#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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@@ -32,8 +33,8 @@ def get_images(prompt):
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def magic_whisper_to_sd(audio, guidance_scale, nb_iterations, seed):
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whisper_results =
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prompt = whisper_results[
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images = get_images(prompt)
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return whisper_results[0], whisper_results[1], whisper_results[2], images
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@@ -75,46 +76,61 @@ def magic_whisper_to_sd(audio, guidance_scale, nb_iterations, seed):
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#
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# return images
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def
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print("""
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β
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Sending audio to Whisper ...
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β
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""")
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date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
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print('DateTime String:', date_time_str)
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audio = whisper.load_audio(audio)
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audio = whisper.pad_or_trim(audio)
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mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
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_, probs = whisper_model.detect_language(mel)
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transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False)
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translate_options = whisper.DecodingOptions(task="translate", fp16 = False)
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transcription = whisper.decode(whisper_model, mel, transcript_options)
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translation = whisper.decode(whisper_model, mel, translate_options)
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print("language spoken: " + transcription.language)
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print("transcript: " + transcription.text)
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print("βββββββββββββββββββββββββββββββββββββββββββ")
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print("translated: " +
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### ββββββββββββββββββββββββββββββββββββββββ
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css = """
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.container {
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max-width:
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margin: auto;
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padding-top: 1.5rem;
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}
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@@ -339,7 +355,7 @@ with gr.Blocks(css=css) as demo:
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lines=3,
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elem_id="transcripted"
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)
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language_detected_output = gr.Textbox(label="Native language", elem_id="spoken_lang",lines=3)
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with gr.Column():
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translated_output = gr.Textbox(
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@@ -406,18 +422,18 @@ with gr.Blocks(css=css) as demo:
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""", elem_id="about")
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audio_r_translate.click(
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inputs = record_input,
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outputs = [
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language_detected_output,
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transcripted_output,
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translated_output
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])
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audio_u_translate.click(
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inputs = upload_input,
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outputs = [
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language_detected_output,
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transcripted_output,
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translated_output
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])
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@@ -430,7 +446,7 @@ with gr.Blocks(css=css) as demo:
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seed
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],
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outputs = [
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language_detected_output,
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transcripted_output,
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translated_output,
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sd_output
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@@ -444,7 +460,7 @@ with gr.Blocks(css=css) as demo:
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seed
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],
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outputs = [
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language_detected_output,
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transcripted_output,
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translated_output,
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sd_output
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import gradio as gr
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#import torch
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#import whisper
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#from datetime import datetime
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from PIL import Image
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#import flag
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import os
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#MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
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#from diffusers import StableDiffusionPipeline
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whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2")
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stable_diffusion = gr.Blocks.load(name="spaces/runwayml/stable-diffusion-v1-5")
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### ββββββββββββββββββββββββββββββββββββββββ
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### ββββββββββββββββββββββββββββββββββββββββ
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#whisper_model = whisper.load_model("small")
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#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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def magic_whisper_to_sd(audio, guidance_scale, nb_iterations, seed):
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whisper_results = translate_better(audio)
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prompt = whisper_results[1]
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images = get_images(prompt)
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return whisper_results[0], whisper_results[1], whisper_results[2], images
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#
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# return images
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def translate_better(audio):
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print("""
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β
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Sending audio to Whisper ...
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β
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""")
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transcribe_text_result = whisper(audio, None, "transcribe", fn_index=0)
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translate_text_result = whisper(audio, None, "translate", fn_index=0)
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print("transcript: " + transcribe_text_result)
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print("βββββββββββββββββββββββββββββββββββββββββββ")
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print("translated: " + translate_text_result)
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return transcribe_text_result, translate_text_result
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#def translate(audio):
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# print("""
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# β
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# Sending audio to Whisper ...
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# β
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# """)
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# # current dateTime
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# now = datetime.now()
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# # convert to string
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# date_time_str = now.strftime("%Y-%m-%d %H:%M:%S")
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# print('DateTime String:', date_time_str)
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#
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# audio = whisper.load_audio(audio)
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# audio = whisper.pad_or_trim(audio)
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#
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# mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
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#
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# _, probs = whisper_model.detect_language(mel)
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#
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# transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False)
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# translate_options = whisper.DecodingOptions(task="translate", fp16 = False)
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#
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# transcription = whisper.decode(whisper_model, mel, transcript_options)
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# translation = whisper.decode(whisper_model, mel, translate_options)
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#
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# print("language spoken: " + transcription.language)
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# print("transcript: " + transcription.text)
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# print("βββββββββββββββββββββββββββββββββββββββββββ")
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# print("translated: " + translation.text)
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# if transcription.language == "en":
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# tr_flag = flag.flag('GB')
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# else:
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# tr_flag = flag.flag(transcription.language)
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# return tr_flag, transcription.text, translation.text
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### ββββββββββββββββββββββββββββββββββββββββ
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css = """
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.container {
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max-width: 780px;
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margin: auto;
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padding-top: 1.5rem;
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}
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lines=3,
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elem_id="transcripted"
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)
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#language_detected_output = gr.Textbox(label="Native language", elem_id="spoken_lang",lines=3)
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with gr.Column():
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translated_output = gr.Textbox(
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""", elem_id="about")
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audio_r_translate.click(translate_better,
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inputs = record_input,
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outputs = [
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#language_detected_output,
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transcripted_output,
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translated_output
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])
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audio_u_translate.click(translate_better,
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inputs = upload_input,
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outputs = [
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#language_detected_output,
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transcripted_output,
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translated_output
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])
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seed
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],
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outputs = [
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#language_detected_output,
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transcripted_output,
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translated_output,
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sd_output
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seed
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],
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outputs = [
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#language_detected_output,
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transcripted_output,
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translated_output,
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sd_output
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