feat: diff between models

.gitignore

@@ -0,0 +1 @@
+.idea

README.md

@@ -7,6 +7,7 @@ sdk: gradio
 sdk_version: 6.8.0
 app_file: app.py
 pinned: false
+python_version: "3.12"
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+import torchaudio
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+import gradio as gr
+
+# # asr_processor_2 = Wav2Vec2Processor.from_pretrained("")
+# # asr_model_2 = Wav2Vec2ForCTC.from_pretrained("")
+
+# phonemes
+asr_1 = pipeline("automatic-speech-recognition", model="FatimahEmadEldin/wav2vec2-xls-r-300m-iqraeval")
+
+# syllables
+asr_2 = pipeline("automatic-speech-recognition", model="IbrahimSalah/Arabic_speech_Syllables_recognition_Using_Wav2vec2")
+
+# text without diacritics
+asr_5 = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-arabic")
+
+# text with diacritics
+asr_4 = pipeline("automatic-speech-recognition", model="rabah2026/wav2vec2-large-xlsr-53-arabic-quran-v_final")
+
+# put syllables into words
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+text_to_text_model = AutoModelForSeq2SeqLM.from_pretrained("IbrahimSalah/Arabic_Syllables_to_text_Converter_Using_MT5")
+text_to_text_tokenizer = AutoTokenizer.from_pretrained("IbrahimSalah/Arabic_Syllables_to_text_Converter_Using_MT5")
+text_to_text_model.eval()
+text_to_text_model.to(device)
+
+
+def transcribe_custom(audio_path, processor, model):
+    # Load and resample audio
+    wav, sr = torchaudio.load(audio_path)
+    if sr != 16000:
+        wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)(wav)
+
+    inputs = processor(wav.squeeze(), sampling_rate=16000, return_tensors="pt")
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    print("---")
+    print(logits)
+    pred_ids = torch.argmax(logits, dim=-1)
+    print(pred_ids)
+    transcription = processor.batch_decode(pred_ids)[0]
+    print(transcription)
+    print("+++")
+    return transcription
+
+
+def transcribe(audio_path):
+    syllables = asr_2(audio_path)['text']
+    seq = "|" + syllables.replace(" ", "|") + "."
+    input_ids = text_to_text_tokenizer.encode(seq, return_tensors="pt").to(device)
+    out_ids = text_to_text_model.generate(
+        input_ids,
+        max_length=max(512, input_ids.shape[1] * 2),
+        repetition_penalty=1.0,
+        num_beams=1,
+        do_sample=False,
+        pad_token_id=text_to_text_tokenizer.pad_token_id,
+        bos_token_id=text_to_text_tokenizer.bos_token_id,
+        eos_token_id=text_to_text_tokenizer.eos_token_id,
+        no_repeat_ngram_size=3,
+    )
+    syllables_to_words = text_to_text_tokenizer.decode(out_ids[0][1:], skip_special_tokens=True).split('.')[0]
+
+    return (
+        asr_1(audio_path)['text'],
+        syllables,
+        syllables_to_words,
+        #transcribe_custom(audio_path, asr_processor_2, asr_model_2),
+        asr_4(audio_path)['text'],
+        asr_5(audio_path)['text'],
+    )
+
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(label="Audio", type="filepath"),
+    outputs=[
+        gr.Textbox(label=f"Transcription {asr_1.model.name_or_path}"),
+        gr.Textbox(label=f"Transcription {asr_2.model.name_or_path}"),
+        gr.Textbox(label=f"Syllables to Words {asr_2.model.name_or_path}"),
+        gr.Textbox(label=f"Transcription {asr_4.model.name_or_path}"),
+        gr.Textbox(label=f"Transcription {asr_5.model.name_or_path}"),
+    ],
+    title="Diff ASR Arabic Models",
+    description="Upload an Arabic audio file.",
+)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch
+torchaudio
+torchcodec
+kenlm
+pyctcdecode