Update app.py

app.py

@@ -1,21 +1,10 @@
 import torch
 import gradio as gr
-import yt_dlp as youtube_dl
-import numpy as np
-from datasets import Dataset, Audio
-from scipy.io import wavfile
 from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-import tempfile
-import os
-import time
-import demucs.api
+from scipy.io import wavfile
 
-MODEL_NAME = "openai/whisper-large-v3" # "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram" # 
-DEMUCS_MODEL_NAME = "htdemucs_ft"
+MODEL_NAME = "openai/whisper-large-v3"
 BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
 
 device = 0 if torch.cuda.is_available() else "cpu"
 
@@ -26,181 +15,30 @@ pipe = pipeline(
     device=device,
 )
 
-separator = demucs.api.Separator(model=DEMUCS_MODEL_NAME, )
-
-def separate_vocal(path):
-    origin, separated = separator.separate_audio_file(path)
-    demucs.api.save_audio(separated["vocals"], path, samplerate=separator.samplerate)
-    return path
-
-def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token: gr.OAuthToken | None, progress=gr.Progress()):
+def transcribe_simple(inputs_path, task):
     if inputs_path is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-    if dataset_name is None:
-        raise gr.Error("No dataset name submitted! Please submit a dataset name. Should be in the format : <user>/<dataset_name> or <org>/<dataset_name>. Also accepts <dataset_name>, which will default to the namespace of the logged-in user.")
-
-    if oauth_token is None:
-        gr.Warning("Make sure to click and login before using this demo.")
-        return [["transcripts will appear here"]], ""
-    
-    total_step = 4
-    current_step = 0
     
-    current_step += 1
-    progress((current_step, total_step), desc="Transcribe using Whisper.")
-
     sampling_rate, inputs = wavfile.read(inputs_path) 
-
     out = pipe(inputs_path, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
-    
     text = out["text"]
     
-    current_step += 1
-    progress((current_step, total_step), desc="Merge chunks.")
-    chunks = naive_postprocess_whisper_chunks(out["chunks"], inputs, sampling_rate)
-
-    current_step += 1
-    progress((current_step, total_step), desc="Create dataset.")
-    
-    transcripts = []
-    audios = []
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        for i, chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for)")):
-            arr = chunk["audio"]
-            path = os.path.join(tmpdirname, f"{i}.wav")
-            wavfile.write(path, sampling_rate, arr)
-            
-            if use_demucs == "separate-audio":
-                print(f"Separating vocals #{i}")
-                path = separate_vocal(path)
-                
-            audios.append(path)
-            transcripts.append(chunk["text"])
-            
-        dataset = Dataset.from_dict({"audio": audios, "text": transcripts}).cast_column("audio", Audio())
-        
-        current_step += 1
-        progress((current_step, total_step), desc="Push dataset.")
-        dataset.push_to_hub(dataset_name, token=oauth_token.token if oauth_token else oauth_token)
-        
-    return [[transcript] for transcript in transcripts], text
-
-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 [[transcript] for transcript in text.split(".") if transcript], text
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(source="upload", type="filepath", label="Upload Audio")
+            task_input = gr.Dropdown(choices=["transcribe", "translate"], value="transcribe", label="Task")
+            submit_button = gr.Button("Transcribe")
+        with gr.Column():
+            output_text = gr.Dataframe(label="Transcripts")
+            output_full_text = gr.Textbox(label="Full Text")
+    
+    submit_button.click(
+        transcribe_simple,
+        inputs=[audio_input, task_input],
+        outputs=[output_text, output_full_text],
     )
-    return HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
-    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
-def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthToken | None, max_filesize=75.0, dataset_sampling_rate=24000, progress=gr.Progress()):
-    if yt_url is None:
-        raise gr.Error("No youtube link submitted! Please put a working link.")
-    if dataset_name is None:
-        raise gr.Error("No dataset name submitted! Please submit a dataset name. Should be in the format : <user>/<dataset_name> or <org>/<dataset_name>. Also accepts <dataset_name>, which will default to the namespace of the logged-in user.")
-
-    total_step = 5
-    current_step = 0
-    
-    html_embed_str = _return_yt_html_embed(yt_url)
-
-    if oauth_token is None:
-        gr.Warning("Make sure to click and login before using this demo.")
-        return html_embed_str, [["transcripts will appear here"]], ""
-        
-    current_step += 1
-    progress((current_step, total_step), desc="Load video.")
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        filepath = os.path.join(tmpdirname, "video.mp4")
-        
-        download_yt_audio(yt_url, filepath)
-        with open(filepath, "rb") as f:
-            inputs_path = f.read()
-
-        inputs = ffmpeg_read(inputs_path, pipe.feature_extractor.sampling_rate)
-        inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-    
-        current_step += 1
-        progress((current_step, total_step), desc="Transcribe using Whisper.")
-        out = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
-        
-        text = out["text"]
-            
-        inputs = ffmpeg_read(inputs_path, dataset_sampling_rate)
-    
-    current_step += 1
-    progress((current_step, total_step), desc="Merge chunks.")
-    chunks = naive_postprocess_whisper_chunks(out["chunks"], inputs, dataset_sampling_rate)
-
-    current_step += 1
-    progress((current_step, total_step), desc="Create dataset.")
-    
-    transcripts = []
-    audios = []
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        for i, chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for).")):
-            arr = chunk["audio"]
-            path = os.path.join(tmpdirname, f"{i}.wav")
-            wavfile.write(path, dataset_sampling_rate, arr)
-            
-            if use_demucs == "separate-audio":
-                print(f"Separating vocals #{i}")
-                path = separate_vocal(path)
-                
-            audios.append(path)
-            transcripts.append(chunk["text"])
-            
-        dataset = Dataset.from_dict({"audio": audios, "text": transcripts}).cast_column("audio", Audio())
-        
-        current_step += 1
-        progress((current_step, total_step), desc="Push dataset.")
-        dataset.push_to_hub(dataset_name, token=oauth_token.token if oauth_token else oauth_token)
-        
-    return html_embed_str, [[transcript] for transcript in transcripts], text
 
-def naive_postprocess_whisper_chunks(chunks, audio_array, sampling_rate, stop_chars=".!:;?", min_duration=5):
-    min_duration = int(min_duration * sampling_rate)
-    new_chunks = []
-    while chunks:
-        current_chunk = chunks.pop(0)
-        begin, end = current_chunk["timestamp"]
-        begin, end = int(begin * sampling_rate), int(end * sampling_rate)
-        current_dur = end - begin
-        text = current_chunk["text"]
-        chunk_to_concat = [audio_array[begin:end]]
-        while chunks and (text[-1] not in stop_chars or (current_dur < min_duration)):
-            ch = chunks.pop(0)
-            begin, end = ch["timestamp"]
-            begin, end = int(begin * sampling_rate), int(end * sampling_rate)
+demo.launch()