Duplicate from BenDaouda/ASR_Audio_to_wolof

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README.md

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+---
+title: First Space Test
+emoji: 👁
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.27.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: BenDaouda/ASR_Audio_to_wolof
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from transformers import pipeline, AutoTokenizer
+from huggingface_hub import login
+import gradio as gr
+
+login('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')
+
+# Spécifiez le nom du modèle et le jeton d'authentification
+model_name = "BenDaouda/wav2vec2-large-xls-r-300m-wolof-test-coloab"
+token = "vhf_IwkuGBEkyipKSnyJzJcCRSwOSJDvNivOmH"
+
+# Chargez le modèle et le tokenizer en utilisant le jeton d'authentification
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
+model = pipeline("automatic-speech-recognition", model=model_name, tokenizer=tokenizer, use_auth_token=True)
+
+# Utilisez la fonction Gradio avec votre modèle chargé
+def transcribe(audio):
+    result = model(audio)["text"]
+    return result
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text"
+)
+
+iface.launch()

requirements.txt

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+torch
+transformers==4.24.0
+ffmpeg
+sounddevice
+soundfile