Duplicate from asafaya/arabic-audio-transcription

Co-authored-by: Ali Safaya <asafaya@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Arabic Audio Transcription
+emoji: 🎙️
+colorFrom: blue
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: false
+license: cc-by-nc-4.0
+duplicated_from: asafaya/arabic-audio-transcription
+---
+
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`models`: _List[string]_
+HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`datasets`: _List[string]_
+HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+
+import shutil
+import os
+import tempfile
+
+from collections import OrderedDict
+from glob import glob
+
+import numpy
+import torch
+import torchaudio
+import torchaudio.functional as F
+
+from pydub import AudioSegment
+from tqdm import tqdm
+
+from speechbrain.pretrained import VAD
+from speechbrain.pretrained import EncoderASR
+
+import gradio as gr
+
+tempdir = tempfile.mkdtemp()
+
+def read_and_resample(filename, outdir):
+    # load the file
+
+    AudioSegment.from_file(filename).export(f"{filename}.wav", format='wav', parameters=["-ar", "16000", "-ac", '1'])
+    filename = f"{filename}.wav"
+
+    signal, sr = torchaudio.load(filename)
+    if sr != 16_000:
+        # downsample to 16khz and mono
+        resampled = F.resample(signal, sr, 16_000, lowpass_filter_width=128).mean(dim=0).view(1, -1).cpu()
+    else:
+        resampled = signal.mean(dim=0).view(1, -1).cpu()
+
+    # get tmp dir:
+    filename = os.path.basename(filename).split(".")[0]
+
+    # yield segments of 90 minutes.
+    c_size = 60 * 60 * 16_000
+    for i, c in enumerate(range(0, resampled.shape[1], c_size)):
+        tempaudio = os.path.join(outdir, f"{filename}-{i}.wav")
+
+        # save to tmp dir:
+        torchaudio.save(tempaudio, resampled[:, c:c+c_size], 16_000)
+        yield (tempaudio, resampled[:, c:c+c_size])
+
+
+def segment_file(VAD, id, prefix, filename, resampled, output_dir):
+
+    min_chunk_size = 4 # seconds
+    max_allowed_length = 12 # seconds
+    margin = 0.15
+
+    with torch.no_grad():
+        audio_info = VAD.get_speech_segments(filename, apply_energy_VAD=True, len_th=0.5, 
+                                        deactivation_th=0.4, double_check=False, close_th=0.25)
+
+    # save segments:
+    s = -1
+    for _s, _e in audio_info:
+        _s, _e = _s.item(), _e.item()
+
+        _s = max(0, _s - margin)
+        e = min(resampled.size(1) / 16_000, _e + margin)
+
+        if s == -1:
+            s = _s
+
+        chunk_length = e - s
+        if chunk_length > min_chunk_size:
+
+            no_chunks = int(numpy.ceil(chunk_length / max_allowed_length))
+            starts = numpy.linspace(s, e, no_chunks + 1).tolist()
+
+            if chunk_length > max_allowed_length:
+                print("WARNING: segment too long:", chunk_length)
+                print(no_chunks, starts)
+
+            for x in range(no_chunks):
+
+                start = starts[x]
+                end = starts[x + 1]
+
+                local_chunk_length = end - start
+
+                print(f"Saving segment: {start:08.2f}-{end:08.2f}, with length: {local_chunk_length:05.2f} secs")
+                fname = f"{id}-{prefix}-{start:08.2f}-{end:08.2f}.wav"
+
+                # convert from seconds to samples:
+                start = int(start * 16_000)
+                end = int(end * 16_000)
+
+                # save segment:
+                torchaudio.save(os.path.join(output_dir, fname), resampled[:, start:end], 16_000)
+            s = -1
+
+
+def format_time(secs: float):
+    m, s = divmod(secs, 60)
+    h, m = divmod(m, 60)
+    return "%d:%02d:%02d,%03d" % (h, m, s, int(secs * 1000 % 1000))
+
+asr_model = EncoderASR.from_hparams(source="asafaya/hubert-large-arabic-transcribe")
+vad_model = VAD.from_hparams(source="speechbrain/vad-crdnn-libriparty")
+
+def main(filename, generate_srt=False):
+    try:
+        AudioSegment.from_file(filename)
+    except:
+        return "Please upload a valid audio file"
+
+    outdir = os.path.join(tempdir, filename.split("/")[-1].split(".")[0])
+    if not os.path.exists(outdir):
+        os.mkdir(outdir)
+
+    print("Applying VAD to", filename)
+
+    # directory to save
+    segments_dir = os.path.join(outdir, "segments")
+    if os.path.exists(segments_dir):
+        raise Exception(f"Segments directory already exists: {segments_dir}")
+    os.mkdir(segments_dir)
+    print("Saving segments to", segments_dir)
+
+    for c, (tempaudio, resampled) in enumerate(read_and_resample(filename, outdir)):
+        print(f"Segmenting file: {filename}, with length: {resampled.shape[1] / 16_000:05.2f} secs: {tempaudio}")
+        segment_file(vad_model, os.path.basename(tempaudio), c, tempaudio, resampled, segments_dir)
+        # os.remove(tempaudio)
+
+    transcriptions = OrderedDict()
+    files = glob(os.path.join(segments_dir, "*.wav"))
+    print("Start transcribing")
+    for f in tqdm(sorted(files)):
+        try:
+            transcriptions[os.path.basename(f).replace(".wav", "")] = asr_model.transcribe_file(f)
+            # os.remove(os.path.basename(f))
+        except Exception as e:
+            print(e)
+            print("Error transcribing file {}".format(f))
+            print("Skipping...")
+
+    # shutil.rmtree(outdir)
+
+    fo = ""
+    for i, key in enumerate(transcriptions):
+        line = key
+
+        # segment-0-00148.72-00156.97
+        start_sec = float(line.split("-")[-2])
+        end_sec = float(line.split("-")[-1])
+        if len(line) < 2: continue
+
+        if generate_srt:
+            fo += ("{}\n".format(i+1))
+            fo += ("{} --> ".format(format_time(start_sec)))
+            fo += ("{}\n".format(format_time(end_sec)))
+
+        fo += ("{}\n".format(transcriptions[key]))
+        fo += ("\n") if generate_srt else ""
+
+    return fo
+
+outputs = gr.outputs.Textbox(label="Transcription")
+
+title = "Arabic Speech Transcription"
+description = "Simply upload your audio."
+
+gr.Interface(main, [gr.inputs.Audio(label="Arabic Audio File", type="filepath"), "checkbox"], outputs, title=title, description=description, enable_queue=True).launch()

output.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f7174c9be1bd14e7bda67f0578c1c0f75a2270017065ea0e79381c6f406e005
+size 320078

requirements.txt

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+speechbrain==0.5.13
+transformers==4.22.2
+pydub==0.25.1
+gradio==3.8.2