Update app.py

app.py

@@ -13,277 +13,8 @@ import os
 import time
 import demucs.api
 
-MODEL_NAME = "openai/whisper-large-v3"  # "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram" 
-DEMUCS_MODEL_NAME = "htdemucs_ft"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
+# Your imports and other functions remain the same
 
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    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 _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 gr.HTML(value=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_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_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).")):
-            
-            # TODO: make sure 1D or 2D?
-            arr = chunk["audio"]
-            path = os.path.join(tmpdirname, f"{i}.wav")
-            wavfile.write(path, dataset_sampling_rate,  arr)
-            
-            if use_demucs == "separate-audio":
-                # use demucs tp separate vocals
-                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_str, [[transcript] for transcript in transcripts], text
-
-def naive_postprocess_whisper_chunks(chunks, audio_array, sampling_rate,  stop_chars = ".!:;?", min_duration = 5):
-    # merge chunks as long as merged audio duration is lower than min_duration and that a stop character is not met
-    # return list of dictionnaries (text, audio)
-    # min duration is in seconds
-    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)
-            current_dur += end-begin
-            
-            text = "".join([text, ch["text"]])
-            
-            # TODO: add silence ?
-            chunk_to_concat.append(audio_array[begin:end])
-            
-
-        new_chunks.append({
-            "text": text.strip(),
-            "audio": np.concatenate(chunk_to_concat),
-        })
-        print(f"LENGTH CHUNK #{len(new_chunks)}: {current_dur/sampling_rate}s")
-                
-    return new_chunks
-
-css = """
-#intro{
-    max-width: 100%;
-    text-align: center;
-    margin: 0 auto;
-}
-"""
-with gr.Blocks(css=css) as demo:
-    with gr.Row():
-        gr.LoginButton()
-        gr.LogoutButton()
-        
-    with gr.Tab("YouTube"):
-        gr.Markdown("Create your own TTS dataset using Youtube", elem_id="intro")
-        gr.Markdown(        
-    "This demo allows use to create a text-to-speech dataset from an input audio snippet and push it to hub to keep track of it."
-    f"Demo uses the checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to automatically transcribe audio files"
-    " of arbitrary length. It then merge chunks of audio and push it to the hub."
-    )
-        with gr.Row():
-            with gr.Column():
-                audio_youtube = gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")
-                task_youtube = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
-                cleaning_youtube = gr.Radio(["no-post-processing", "separate-audio"], label="Audio separation and cleaning (takes longer - use it if your samples are not cleaned (background noise and music))", value="separate-audio")
-                textbox_youtube = gr.Textbox(lines=1, placeholder="Place your new dataset name here. 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.", label="Dataset name")
-                
-                with gr.Row():
-                    clear_youtube = gr.ClearButton([audio_youtube, task_youtube, cleaning_youtube, textbox_youtube])
-                    submit_youtube = gr.Button("Submit")
-                
-            with gr.Column():
-                html_youtube = gr.HTML()
-                dataset_youtube = gr.Dataset(label="Transcribed samples.", components=["text"], headers=["Transcripts"], samples=[["transcripts will appear here"]])
-                transcript_youtube = gr.Textbox(label="Transcription")
-        
-    with gr.Tab("Microphone or Audio file"):
-        gr.Markdown("Create your own TTS dataset using your own recordings", elem_id="intro")
-        gr.Markdown(        
-    "This demo allows use to create a text-to-speech dataset from an input audio snippet and push it to hub to keep track of it."
-    f"Demo uses the checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to automatically transcribe audio files"
-    " of arbitrary length. It then merge chunks of audio and push it to the hub."
-    )
-        with gr.Row():
-            with gr.Column():
-                audio_file = gr.Audio(type="filepath")
-                task_file = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
-                cleaning_file = gr.Radio(["no-post-processing", "separate-audio"], label="Audio separation and cleaning (takes longer - use it if your samples are not cleaned (background noise and music))", value="no-post-processing")
-                textbox_file = gr.Textbox(lines=1, placeholder="Place your new dataset name here. 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.", label="Dataset name")
-                
-                with gr.Row():
-                    clear_file = gr.ClearButton([audio_file, task_file, cleaning_file, textbox_file])
-                    submit_file = gr.Button("Submit")
-                
-            with gr.Column():
-                dataset_file = gr.Dataset(label="Transcribed samples.", components=["text"], headers=["Transcripts"], samples=[["transcripts will appear here"]])
-                transcript_file = gr.Textbox(label="Transcription")
-
-                def function_transcribe(audio_file, task_file, cleaning_file, textbox_file, max_filesize=75.0, dataset_sampling_rate = 24000, progress=gr.Progress()):
-    if isinstance(audio_file, str):
-        audio_file = open(audio_file, "rb")
-
-    _, extension = os.path.splitext(audio_file.name)
-    if extension != '.mp3' and extension != '.wav':
-        raise RuntimeError("Invalid file format. Supported formats are mp3 and wav.")
-
-    if audio_file.size // (1024 * 1024) > FILE_LIMIT_MB:
-        raise RuntimeError(f"File size exceeds the limit ({extension} file {FILE_LIMIT_MB} MB).")
-
-    task = task_file.lower()
-    if task != "transcribe" and task != "translate":
-        raise RuntimeError("Unsupported task. Task must be either 'transcribe' or 'translate'.")
-
-    cleanup = bool(cleaning_file)
-
-    dataset_name = textbox_file.strip().replace("/", "_").replace(" ", "_")
-
-    audio_content = audio_file.read()
-    audio_array, sample_rate = wavfile.imread(BytesIO(audio_content), "wav")
-
-    chunks = naive_postprocess_whisper_chunks(audio_array, sample_rate, stop_chars=".<>?", min_duration=5)
-
-    texts = whisper_batch_transcribe(chunks, model=MODEL_NAME, device=device, task=task)
-
-    if cleanup:
-        cleaned_chunks = clean_audio_chunks(chunks, audio_array, sample_rate)
-        cleaned_texts = whisper_batch_transcribe(cleaned_chunks, model=MODEL_NAME, device=device, task=task)
-        texts = cleaned_texts
-
-    texts = [t.strip() for t in texts]
-
-    dataset = Dataset.from_dict({"text": texts})
-    if dataset_name:
-        dataset.push_to_hub(dataset_name, repo_type="dataset", private=True)
-
-    return dataset, "\n\n".join(texts)
-
-    submit_file.click(function_transcribe, inputs=[audio_file, task_file, cleaning_file, textbox_file], outputs=[dataset_file, transcript_file])                
-    submit_youtube.click(yt_transcribe, inputs=[audio_youtube, task_youtube, cleaning_youtube, textbox_youtube], outputs=[html_youtube, dataset_youtube, transcript_youtube])                
 def function_transcribe(audio_file, task_file, cleaning_file, textbox_file, max_filesize=75.0, dataset_sampling_rate = 24000, progress=gr.Progress()):
     if isinstance(audio_file, str):
         audio_file = open(audio_file, "rb")
@@ -323,4 +54,6 @@ def function_transcribe(audio_file, task_file, cleaning_file, textbox_file, max_
 
     return dataset, "\n\n".join(texts)
 
-demo.launch(debug=True)   
+# Continuing with the rest of the script
+
+demo.launch(debug=True)