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README.md

@@ -1,12 +1,6 @@
 ---
-title: Automatic Speech Recognition German
-emoji: 🦀
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.38.0
+title: automatic-speech-recognition-german
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.36.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,54 @@
+import torch
+import gradio as gr
+from transformers import pipeline
+
+model_id = "openai/whisper-large-v2"  # update with your model id
+pipe = pipeline("automatic-speech-recognition", model=model_id)
+
+
+title = "Automatic Speech Recognition (ASR)"
+description = """
+Demo for automatic speech recognition in German. Demo uses [Sandiago21/whisper-large-v2-german](https://huggingface.co/Sandiago21/whisper-large-v2-german) checkpoint, which is based on OpenAI's
+[Whisper](https://huggingface.co/openai/whisper-large-v2) model and is fine-tuned in German Audio dataset
+![Automatic Speech Recognition (ASR)"](https://datasets-server.huggingface.co/assets/huggingface-course/audio-course-images/--/huggingface-course--audio-course-images/train/2/image/image.png "Diagram of Automatic Speech Recognition (ASR)")
+"""
+
+def transcribe_speech(filepath):
+    output = pipe(
+        filepath,
+        max_new_tokens=256,
+        generate_kwargs={
+            "task": "transcribe",
+            "language": "german",
+        },  # update with the language you've fine-tuned on
+        chunk_length_s=30,
+        batch_size=8,
+    )
+    return output["text"]
+
+demo = gr.Blocks()
+
+mic_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=gr.outputs.Textbox(),
+    tilte=title,
+    description=description,
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=gr.outputs.Textbox(),
+    examples=[["./example.wav"]],
+    tilte=title,
+    description=description,
+)
+
+with demo:
+    gr.TabbedInterface(
+        [mic_transcribe, file_transcribe],
+        ["Transcribe Microphone", "Transcribe Audio File"],
+    ),
+
+demo.launch()

requirements.txt

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+transformers
+torch