EchoScript : Add application file

app.py

@@ -0,0 +1,123 @@
+import tempfile
+from pathlib import Path
+
+import gradio as gr
+from faster_whisper import WhisperModel
+
+MODEL_SIZE = "base"
+
+model = WhisperModel(
+    MODEL_SIZE,
+    device="cpu",
+    compute_type="int8"
+)
+
+LANGUAGE_NAMES = {
+    "fr": "French",
+    "en": "English",
+    "de": "German",
+    "fa": "Persian",
+    "es": "Spanish",
+    "it": "Italian",
+    "pt": "Portuguese",
+    "nl": "Dutch"
+}
+
+
+def transcribe(audio_file):
+
+    if audio_file is None:
+        return "", "", None
+
+    segments, info = model.transcribe(
+        audio_file,
+        beam_size=5
+    )
+
+    transcript_lines = []
+    timestamp_lines = []
+
+    for segment in segments:
+        transcript_lines.append(segment.text)
+egment.end:.2f}s] "
+            f"{segment.text}"
+        )
+
+    transcript = "\n".join(transcript_lines)
+
+    transcript_with_timestamps = "\n".join(
+        timestamp_lines
+    )
+
+    detected_language = LANGUAGE_NAMES.get(
+        info.language,
+        info.language
+    )
+
+    summary = (
+        f"Detected language: {detected_language}\n"
+        f"Confidence: {info.language_probability:.2%}"
+    )
+
+    output_file = Path(tempfile.gettempdir()) / "transcript.txt"
+
+    with open(
+        output_file,
+        "w",
+        encoding="utf-8"
+    ) as f:
+        f.write(transcript_with_timestamps)
+
+    return (
+        summary,
+        transcript,
+        str(output_file)
+    )
+
+
+with gr.Blocks(title="EchoScript") as demo:
+
+    gr.Markdown(
+        """
+        # EchoScript
+
+        Upload an audio file and automatically
+        transcribe speech to text.
+        """
+    )
+
+    audio_input = gr.Audio(
+        type="filepath",
+        label="Upload Audio"
+    )
+
+    transcribe_button = gr.Button(
+        "Transcribe"
+    )
+
+    language_output = gr.Textbox(
+        label="Language Information"
+    )
+
+    transcript_output = gr.Textbox(
+        label="Transcript",
+        lines=20
+    )
+
+    download_output = gr.File(
+        label="Download Transcript"
+    )
+
+    transcribe_button.click(
+        fn=transcribe,
+        inputs=audio_input,
+        outputs=[
+            language_output,
+            transcript_output,
+            download_output
+        ]
+    )
+
+demo.launch()
+        timestamp_lines.append(
+            f"[{segment.start:.2f}s �