add app.py

app.py

@@ -0,0 +1,83 @@
+import torch
+from transformers import pipeline
+import gradio as gr
+
+MODEL_NAME = "JackismyShephard/whisper-tiny-finetuned-minds14"
+BATCH_SIZE = 8
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+
+
+# Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
+def format_timestamp(
+    seconds: float, always_include_hours: bool = False, decimal_marker: str = "."
+):
+    if seconds is not None:
+        milliseconds = round(seconds * 1000.0)
+
+        hours = milliseconds // 3_600_000
+        milliseconds -= hours * 3_600_000
+
+        minutes = milliseconds // 60_000
+        milliseconds -= minutes * 60_000
+
+        seconds = milliseconds // 1_000
+        milliseconds -= seconds * 1_000
+
+        hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+        return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+    else:
+        # we have a malformed timestamp so just return it as is
+        return seconds
+
+
+def transcribe(file, task, return_timestamps):
+    outputs = pipe(
+        file,
+        batch_size=BATCH_SIZE,
+        generate_kwargs={
+            "task": task,
+            "language": "english",
+        },
+        return_timestamps=return_timestamps,
+    )
+    text = outputs["text"]
+    if return_timestamps:
+        timestamps = outputs["chunks"]
+        timestamps = [
+            f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
+            for chunk in timestamps
+        ]
+        text = "\n".join(str(feature) for feature in timestamps)
+    return text
+
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(label="Audio", type="filepath"),
+        gr.inputs.Radio(
+            ["transcribe", "translate"], label="Task", default="transcribe"
+        ),
+        gr.inputs.Checkbox(default=False, label="Return timestamps"),
+    ],
+    outputs=gr.Textbox(),
+    title="Automatic Speech Recognition",
+    description=(
+        "Transcribe or translate long-form microphone or audio inputs with the click of a button! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe or translate audio files"
+        " of arbitrary length."
+    ),
+    examples="./examples",
+    cache_examples=True,
+    allow_flagging="never",
+)
+
+demo.launch()