Spaces:
Runtime error
Runtime error
| 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="Neprox/model") | |
| def download_from_youtube(url): | |
| streams = YouTube(url).streams.filter(only_audio=True, file_extension='mp4') | |
| fpath = streams.first().download() | |
| return fpath | |
| def get_timestamp(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): | |
| 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): | |
| # TODO: Make API call to Google Translate to get English translation | |
| return "..." | |
| def transcribe(audio, url, seconds_max): | |
| 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 | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", label="Transcribe from Microphone"), | |
| gr.Text(max_lines=1, placeholder="Enter YouTube Link with Swedish speech to be transcribed", 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 Swedish", | |
| description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.", | |
| ) | |
| iface.launch() | |