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"""app.py |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1sjpiZeNESzMVcNpywmkNPS6Wwu6pTfrE |
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""" |
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import os |
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import gradio as gr |
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from transformers import pipeline |
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from pytube import YouTube |
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from datasets import Dataset, Audio |
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from moviepy.editor import AudioFileClip |
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pipe = pipeline(model="Qinglinz/whisper-small-hi") |
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def download_from_youtube(url): |
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""" |
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Downloads the video from the given YouTube URL and returns the path to the audio file. |
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""" |
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streams = YouTube(url).streams.filter(only_audio=True, file_extension='mp4') |
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fpath = streams.first().download() |
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return fpath |
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def get_timestamp(seconds): |
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""" |
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Creates %M:%S timestamp from seconds. |
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""" |
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minutes = int(seconds / 60) |
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seconds = int(seconds % 60) |
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return f"{str(minutes).zfill(2)}:{str(seconds).zfill(2)}" |
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def divide_into_30s_segments(audio_fpath, seconds_max): |
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""" |
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Divides the audio file into 30s segments and returns the paths to the segments and the start times of the segments. |
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:param audio_fpath: Path to the audio file. |
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:param seconds_max: Maximum number of seconds to consider. If the audio file is longer than this, it will be truncated. |
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""" |
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if not os.path.exists("segmented_audios"): |
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os.makedirs("segmented_audios") |
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sound = AudioFileClip(audio_fpath) |
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n_full_segments = int(sound.duration / 30) |
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len_last_segment = sound.duration % 30 |
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max_segments = int(seconds_max / 30) |
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if n_full_segments > max_segments: |
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n_full_segments = max_segments |
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len_last_segment = 0 |
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segment_paths = [] |
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segment_start_times = [] |
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segments_available = n_full_segments + 1 |
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for i in range(min(segments_available, max_segments)): |
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start = i * 30 |
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is_last_segment = i == n_full_segments |
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if is_last_segment and not len_last_segment > 2: |
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continue |
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elif is_last_segment: |
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end = start + len_last_segment |
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else: |
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end = (i + 1) * 30 |
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segment_path = os.path.join("segmented_audios", f"segment_{i}.wav") |
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segment = sound.subclip(start, end) |
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segment.write_audiofile(segment_path) |
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segment_paths.append(segment_path) |
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segment_start_times.append(start) |
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return segment_paths, segment_start_times |
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def get_translation(text): |
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""" |
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Translates the given Chinese text to English. |
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""" |
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return "TODO: Make API call to Google Translate to get English translation" |
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def transcribe(audio, url, seconds_max): |
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""" |
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Transcribes a YouTube video if a url is specified and returns the transcription. |
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If not url is specified, it transcribes the audio file as passed by Gradio. |
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:param audio: Audio file as passed by Gradio. Only used if no url is specified. |
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:param url: YouTube URL to transcribe. |
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:param seconds_max: Maximum number of seconds to consider. If the audio file is longer than this, it will be truncated. |
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""" |
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if url: |
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fpath = download_from_youtube(url) |
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segment_paths, segment_start_times = divide_into_30s_segments(fpath, seconds_max) |
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audio_dataset = Dataset.from_dict({"audio": segment_paths}).cast_column("audio", Audio(sampling_rate=16000)) |
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pred = pipe(audio_dataset["audio"]) |
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text = "" |
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n_segments = len(segment_start_times) |
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for i, (seconds, output) in enumerate(zip(segment_start_times, pred)): |
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text += f"[Segment {i+1}/{n_segments}, start time {get_timestamp(seconds)}]\n" |
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text += f"{output['text']}\n" |
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text += f"[Translation]\n{get_translation(output['text'])}\n\n" |
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return text |
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else: |
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text = pipe(audio)["text"] |
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return text |
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block = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.Audio(source="microphone", type="filepath", label="Transcribe from Microphone"), |
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gr.Text(max_lines=1, placeholder="Enter YouTube Link which has a Swedish video", label="Transcribe from YouTube URL"), |
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gr.Slider(minimum=30, maximum=300, value=30, step=30, label="Number of seconds to transcribe from YouTube URL") |
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], |
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outputs="text", |
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title="Whisper Small Swedish", |
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description="Realtime Swedish speech recognition", |
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) |
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block.launch() |
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