Create app.py

app.py

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+import gradio as gr
+import requests
+import pytube
+from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE
+from transformers.pipelines.audio_utils import ffmpeg_read
+
+title = "Whisper JAX: The Fastest Whisper API Available ⚡️"
+
+description = """Whisper JAX is an optimised implementation of the [Whisper model](https://huggingface.co/openai/whisper-large-v2) by OpenAI. It runs on JAX with a TPU v4-8 in the backend. Compared to PyTorch on an A100 GPU, it is over **12x** faster, making it the fastest Whisper API available.
+
+You can submit requests to Whisper JAX through this Gradio Demo, or directly through API calls (see below). This notebook demonstrates how you can run the Whisper JAX model yourself on a TPU v2-8 in a Google Colab: TODO.
+"""
+
+API_URL = "https://whisper-jax.ngrok.io/generate/"
+
+api_info = """## Python API call:
+```python
+import requests
+
+response = requests.post("{URL}", json={
+  "inputs": "/path/to/file/audio.mp3",
+  "task": "transcribe",
+  "return_timestamps": False,
+}).json()
+
+data = response["data"]
+```
+
+## Javascript API call:
+```javascript
+fetch("{URL}", {
+  method: "POST",
+  headers: { "Content-Type": "application/json" },
+  body: JSON.stringify({
+    data: [
+      "/path/to/file/audio.mp3",
+      "afrikaans",
+      "transcribe",
+      false,
+	]
+  })})
+.then(r => r.json())
+.then(
+  r => {
+    let data = r.data;
+  }
+)
+```
+
+## CURL API call:
+```
+curl -X POST -d '{"inputs": "/path/to/file/audio.mp3", "task": "transcribe", "return_timestamps": false}' {URL} -H "content-type: application/json"
+```
+"""
+api_info = api_info.replace("{URL}", API_URL)
+article = "Whisper large-v2 model by OpenAI. Backend running JAX on a TPU v4-8 through the generous support of the [TRC](https://sites.research.google/trc/about/) programme."
+
+language_names = sorted(TO_LANGUAGE_CODE.keys())
+SAMPLING_RATE = 16000
+
+
+def query(payload):
+    response = requests.post(API_URL, json=payload)
+    return response.json(), response.status_code
+
+
+def inference(inputs, task, return_timestamps):
+    payload = {"inputs": inputs, "task": task, "return_timestamps": return_timestamps}
+
+    data, status_code = query(payload)
+
+    if status_code == 200:
+        text = data["text"]
+    else:
+        text = data["detail"]
+
+    if return_timestamps:
+        timestamps = data[0]["chunks"]
+    else:
+        timestamps = None
+
+    return text, timestamps
+
+
+def transcribe_audio(microphone, file_upload, task, return_timestamps):
+    warn_output = ""
+    if (microphone is not None) and (file_upload is not None):
+        warn_output = (
+            "WARNING: You've uploaded an audio file and used the microphone. "
+            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+        )
+
+    elif (microphone is None) and (file_upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+
+    inputs = microphone if microphone is not None else file_upload
+
+    inputs = {"array": inputs[1].tolist(), "sampling_rate": inputs[0]}
+
+    text, timestamps = inference(inputs=inputs, task=task, return_timestamps=return_timestamps)
+
+    return warn_output + text, timestamps
+
+
+def _return_yt_html_embed(yt_url):
+    video_id = yt_url.split("?v=")[-1]
+    HTML_str = (
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        " </center>"
+    )
+    return HTML_str
+
+
+def transcribe_youtube(yt_url, task, return_timestamps):
+    yt = pytube.YouTube(yt_url)
+    html_embed_str = _return_yt_html_embed(yt_url)
+    stream = yt.streams.filter(only_audio=True)[0]
+    stream.download(filename="audio.mp3")
+
+    with open("audio.mp3", "rb") as f:
+        inputs = f.read()
+
+    inputs = ffmpeg_read(inputs, SAMPLING_RATE)
+    inputs = {"array": inputs.tolist(), "sampling_rate": SAMPLING_RATE}
+
+    yield html_embed_str, "Video loaded, transcribing audio...", None
+
+    text, timestamps = inference(inputs=inputs, task=task, return_timestamps=return_timestamps)
+
+    yield html_embed_str, text, timestamps
+
+audio = gr.Interface(
+    fn=transcribe_audio,
+    inputs=[
+        gr.inputs.Audio(source="microphone", optional=True),
+        gr.inputs.Audio(source="upload", optional=True),
+        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+        gr.inputs.Checkbox(default=False, label="Return timestamps"),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Transcription"),
+        gr.outputs.Textbox(label="Timestamps"),
+    ],
+    allow_flagging="never",
+    title=title,
+    description=description,
+    article=article,
+)
+
+youtube = gr.Interface(
+    fn=transcribe_youtube,
+    inputs=[
+        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
+        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+        gr.inputs.Checkbox(default=False, label="Return timestamps"),
+    ],
+    outputs=[
+        gr.outputs.HTML(label="Video"),
+        gr.outputs.Textbox(label="Transcription"),
+        gr.outputs.Textbox(label="Timestamps"),
+    ],
+    allow_flagging="never",
+    title=title,
+    description=description,
+    article=article,
+)
+
+demo = gr.Blocks()
+
+with demo:
+    gr.TabbedInterface([audio, youtube], ["Transcribe Audio", "Transcribe YouTube"])
+
+demo.queue()
+demo.launch()