Update app.py

app.py

@@ -1,6 +1,6 @@
 import torch
 import gradio as gr
-import yt_dlp as youtube_dl
+import youtube_dl
 import numpy as np
 from datasets import Dataset, Audio
 from scipy.io import wavfile
@@ -13,47 +13,240 @@ import os
 import time
 import demucs.api
 
-# Your imports and other functions remain the same
-
-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)
-
-# Continuing with the rest of the script
-
-demo.launch(debug=True)
+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
+
+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")
+                
+                
+    
+    submit_file.click(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])                
+    
+demo.launch(debug=True)
+Confío en que