fix

.gitignore

@@ -0,0 +1 @@
+.idea

README.md

@@ -1,12 +1,13 @@
 ---
-title: Kotoba Whisper Bilingual Demo
-emoji: 📚
-colorFrom: green
-colorTo: purple
+title: Kotoba Whisper Demo
+emoji: 🔥
+colorFrom: yellow
+colorTo: blue
 sdk: gradio
-sdk_version: 4.44.0
+sdk_version:  4.39.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+from math import floor
+from typing import Optional
+
+import spaces
+import torch
+import gradio as gr
+from transformers import pipeline
+from transformers.pipelines.audio_utils import ffmpeg_read
+
+
+# configuration
+MODEL_NAME = "japanese-asr/distil-whisper-bilingual-v1.0"
+BATCH_SIZE = 16
+CHUNK_LENGTH_S = 15
+# device setting
+if torch.cuda.is_available():
+    torch_dtype = torch.bfloat16
+    device = "cuda"
+    model_kwargs = {'attn_implementation': 'sdpa'}
+else:
+    torch_dtype = torch.float32
+    device = "cpu"
+    model_kwargs = {}
+
+# define the pipeline
+pipe = pipeline(
+    model=MODEL_NAME,
+    chunk_length_s=CHUNK_LENGTH_S,
+    batch_size=BATCH_SIZE,
+    torch_dtype=torch_dtype,
+    device=device,
+    model_kwargs=model_kwargs,
+    trust_remote_code=True
+)
+
+
+def format_time(start: Optional[float], end: Optional[float]):
+
+    def _format_time(seconds: Optional[float]):
+        if seconds is None:
+            return "complete    "
+        minutes = floor(seconds / 60)
+        hours = floor(seconds / 3600)
+        seconds = seconds - hours * 3600 - minutes * 60
+        m_seconds = floor(round(seconds - floor(seconds), 3) * 10 ** 3)
+        seconds = floor(seconds)
+        return f'{hours:02}:{minutes:02}:{seconds:02}.{m_seconds:03}'
+
+    return f"[{_format_time(start)}-> {_format_time(end)}]:"
+
+
+@spaces.GPU
+def get_prediction(inputs, task: str, language: Optional[str]):
+    generate_kwargs = {"task": task}
+    if language:
+        generate_kwargs['language'] = language
+    prediction = pipe(inputs, return_timestamps=True, generate_kwargs=generate_kwargs)
+    text = "".join([c['text'] for c in prediction['chunks']])
+    text_timestamped = "\n".join([
+        f"{format_time(*c['timestamp'])} {c['text']}" for c in prediction['chunks']
+    ])
+    return text, text_timestamped
+
+
+def transcribe(inputs: str, task: str, language: str):
+    language = None if language == "none" else language
+    if inputs is None:
+        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
+    with open(inputs, "rb") as f:
+        inputs = f.read()
+    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
+    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
+    return get_prediction(inputs, task, language)
+
+
+demo = gr.Blocks()
+description = (f"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses Kotoba-Whisper "
+               f"checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio"
+               f" files of arbitrary length.")
+title = f"Transcribe Audio with {os.path.basename(MODEL_NAME)}"
+mf_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(sources="microphone", type="filepath"),
+        gr.Textbox(lines=1, placeholder="Prompt"),
+        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+        gr.Radio(["none", "ja", "en"], label="Language", default="none")
+    ],
+    outputs=["text", "text"],
+    title=title,
+    description=description,
+    allow_flagging="never",
+)
+file_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(sources="upload", type="filepath", label="Audio file"),
+        gr.Textbox(lines=1, placeholder="Prompt"),
+        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+        gr.Radio(["none", "ja", "en"], label="Language", default="none")
+    ],
+    outputs=["text", "text"],
+    title=title,
+    description=description,
+    allow_flagging="never",
+)
+with demo:
+    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
+demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False, show_error=True)

packages.txt

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+ffmpeg

requirements.txt

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+git+https://github.com/huggingface/transformers
+torch