Update app.py

app.py

@@ -1,3 +1,139 @@
+
+import os
+import gradio as gr
+from transformers import pipeline
+from pytube import YouTube
+from datasets import Dataset, Audio
+from moviepy.editor import AudioFileClip
+
+pipe = pipeline(model="irena/whisper-small-sv-SE")
+
+def download_from_youtube(url):
+    """
+    Downloads the video from the given YouTube URL and returns the path to the audio file.
+    """
+    streams = YouTube(url).streams.filter(only_audio=True, file_extension='mp4')
+    fpath = streams.first().download()
+    return fpath
+
+def get_timestamp(seconds):
+    """
+    Creates %M:%S timestamp from seconds.
+    """
+    minutes = int(seconds / 60)
+    seconds = int(seconds % 60)
+    return f"{str(minutes).zfill(2)}:{str(seconds).zfill(2)}"
+
+def divide_into_30s_segments(audio_fpath, seconds_max):
+    """
+    Divides the audio file into 30s segments and returns the paths to the segments and the start times of the segments.
+    :param audio_fpath: Path to the audio file.
+    :param seconds_max: Maximum number of seconds to consider. If the audio file is longer than this, it will be truncated.
+    """
+    if not os.path.exists("segmented_audios"):
+        os.makedirs("segmented_audios")
+
+    sound = AudioFileClip(audio_fpath)
+    n_full_segments = int(sound.duration / 30)
+    len_last_segment = sound.duration % 30
+
+    max_segments = int(seconds_max / 30)
+    if n_full_segments > max_segments:
+        n_full_segments = max_segments
+        len_last_segment = 0
+
+    segment_paths = []
+    segment_start_times = []
+
+    segments_available = n_full_segments + 1
+    for i in range(min(segments_available, max_segments)):
+        start = i * 30
+
+        # Skip last segment if it is smaller than two seconds
+        is_last_segment = i == n_full_segments
+        if is_last_segment and not len_last_segment > 2:
+            continue
+        elif is_last_segment:
+            end = start + len_last_segment
+        else:
+            end = (i + 1) * 30
+
+        segment_path = os.path.join("segmented_audios", f"segment_{i}.wav")
+        segment = sound.subclip(start, end)
+        segment.write_audiofile(segment_path)
+        segment_paths.append(segment_path)
+        segment_start_times.append(start)
+
+    return segment_paths, segment_start_times
+def get_translation(text):
+    """
+    Translates the given Chinese text to English.
+    """
+    return "TODO: Make API call to Google Translate to get English translation"
+
+def transcribe(audio, url, seconds_max):
+    """
+    Transcribes a YouTube video if a url is specified and returns the transcription.
+    If not url is specified, it transcribes the audio file as passed by Gradio.
+    :param audio: Audio file as passed by Gradio. Only used if no url is specified.
+    :param url: YouTube URL to transcribe.
+    :param seconds_max: Maximum number of seconds to consider. If the audio file is longer than this, it will be truncated.
+    """
+    if url:
+        fpath = download_from_youtube(url)
+        segment_paths, segment_start_times = divide_into_30s_segments(fpath, seconds_max)
+
+        audio_dataset = Dataset.from_dict({"audio": segment_paths}).cast_column("audio", Audio(sampling_rate=16000))
+        pred = pipe(audio_dataset["audio"])
+        text = ""
+        n_segments = len(segment_start_times)
+        for i, (seconds, output) in enumerate(zip(segment_start_times, pred)):
+            text += f"[Segment {i+1}/{n_segments}, start time {get_timestamp(seconds)}]\n"
+            text += f"{output['text']}\n"
+            text += f"[Translation]\n{get_translation(output['text'])}\n\n"
+        return text
+
+    else:
+        text = pipe(audio)["text"]
+    return text
+    
+block = gr.Interface(
+    fn=transcribe, 
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", label="Transcribe from Microphone"),
+        gr.Text(max_lines=1, placeholder="Enter YouTube Link which has a Chinese video", label="Transcribe from YouTube URL"),
+        gr.Slider(minimum=30, maximum=300, value=30, step=30, label="Number of seconds to transcribe from YouTube URL")
+    ], 
+    outputs="text",
+    title="Whisper Small Chinese",
+    description="Realtime Chinese speech recognition",
+)
+
+block.launch()
+
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+'''
 import os
 import gradio as gr
 from transformers import pipeline
@@ -69,7 +205,7 @@ with demo:
 
 demo.launch(enable_queue=True)
 
-
+'''