Create app.py

app.py

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+import torch
+import spaces
+import gradio as gr
+
+from transformers import pipeline
+from huggingface_hub import model_info
+
+MODEL_NAME = "openai/whisper-small"
+
+device = 0 if torch.cuda.is_available() else "cpu"
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
+pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids( task="transcribe")
+
+@spaces.GPU(duration=240)
+def transcribe(mic, file_upload):
+
+    file = mic if mic is not None else file_upload
+
+    text = pipe(file)["text"]
+    return text
+
+
+#---------------------------------------------------------------
+import ctranslate2
+import gradio as gr
+from huggingface_hub import snapshot_download
+from sentencepiece import SentencePieceProcessor
+
+model_name = "santhosh/madlad400-3b-ct2"
+model_path = snapshot_download(model_name)
+
+tokenizer = SentencePieceProcessor()
+tokenizer.load(f"{model_path}/sentencepiece.model")
+translator = ctranslate2.Translator(model_path)
+tokens = [tokenizer.decode(i) for i in range(460)]
+lang_codes = [token[2:-1] for token in tokens if token.startswith("<2")]
+
+
+@spaces.GPU(duration=240)
+def translate(input_text, target_language):
+    input_tokens = tokenizer.encode(f"<2{target_language}> {input_text}", out_type=str)
+    results = translator.translate_batch(
+        [input_tokens],
+        batch_type="tokens",
+        beam_size=1,
+        no_repeat_ngram_size=1,
+    )
+    translated_sentence = tokenizer.decode(results[0].hypotheses[0])
+    return translated_sentence
+
+
+@spaces.GPU(duration=240)
+def translate_interface(input_text, target_language):
+    translated_text = translate(input_text, target_language)
+    return translated_text
+
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown(
+        """
+       
+        <div style="text-align: left;">
+            <a href='https://huggingface.co/PhuongPhan'><img style='display: inline-block; margin: 0; padding: 0;' src='https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-me-on-HF-sm-dark.svg' alt='Follow me on HF'></a>
+            <a href='https://huggingface.co/Chunte'><img style='display: inline-block; margin: 0; padding: 0;' src='https://img.shields.io/badge/GitHub%20Pages-121013?logo=github&logoColor=white' alt='GitHub Pages'></a>
+        </div>
+
+        """ )
+
+        gr.Markdown("<h1 style='text-align: center;'>🎤 Speech to Text & Translation 🗣️</h1>")
+
+        gr.HTML(
+            "<p style='text-align: center'>"
+                "🐤 <a href='https://huggingface.co/openai/whisper-small' target='_blank'>OpenAI Whisper</a>  | "
+                "🧑‍💻 <a href='https://huggingface.co/google/madlad400-3b-mt' target='_blank'>Google Madlad</a>"
+            "</p>")
+        
+        gr.Markdown("<p style='text-align: center;'><i>Upload an audio file or use your microphone to transcribe speech and then translate it to different languages.</i></p>")    
+    
+    with gr.Row():
+        # First interface for transcription
+        gr.Markdown("## 🎙️ Transcribe Audio ") 
+        gr.Markdown("---")
+        audio_input = gr.Audio(sources=["upload", "microphone"], type="filepath")
+        transcribe_button = gr.Button("Transcribe")
+        transcribed_output = gr.Textbox(label="Transcribed Text")
+        transcribe_button.click(transcribe, inputs=audio_input, outputs=transcribed_output)
+
+    with gr.Row():
+        # Second interface for translation
+        gr.Markdown("## 🌐 Translate Text 🌐")
+        gr.Markdown("---") 
+        lang_dropdown = gr.Dropdown(lang_codes, value="en", label="Target Language")
+        translate_button = gr.Button("Translate")
+        translated_output = gr.Textbox(label="Translated Text")
+        translate_button.click(translate_interface, inputs=[transcribed_output, lang_dropdown], outputs=translated_output)
+
+    gr.Markdown("---")
+    with gr.Accordion("See Details", open = False):
+
+        gr.Markdown("---")
+        gr.Markdown('''
+
+## Description 📝
+
+    > Using OpenAI Whisper Base model to transcribe audio files into text Google Madlad model to translate transcribed texts into multiple languages. 
+    > Enabling users to convert spoken words into written text. 
+    > Supporting various use cases, including transcription of audio files, detection of phrases, speech-to-text generation, and translation of text.
+    
+## How it Works 🫶
+
+    - Upload an audio file or record a new one directly in the app.
+    - Transcribe the audio into text, allow copy and paste function for further use.
+    - Or/ Translates the transcribed text into multiple languages.
+
+## Usage 🤗
+
+    1. Transcribe audio files for note-taking, research, or content creation
+    2. Detect phrases or keywords in audio recordings for data analysis or market research
+    3. Generate text from speech for speech-to-text applications, such as subtitles, closed captions, or voice assistants
+    4. Use the app for language learning, by transcribing audio files in a foreign language and practicing pronunciation
+    5. Translate the transcribed text into multiple languages for global communication
+
+## Disclaimer 🙅‍♂️
+
+> This app is for personal use only and should not be used for commercial purposes.
+The OpenAI Whisper Base model and Google Madlad model are pre-trained models and may not always produce accurate results.   ''')
+
+    demo.queue(max_size=20)
+    demo.launch()