Create run.py

backend/run.py

@@ -0,0 +1,36 @@
+import torch
+from transformers import pipeline
+
+translator = pipeline("automatic-speech-recognition",
+                      "facebook/seamless-m4t-v2-large",
+                      torch_dtype=torch.float32,
+                      device="cpu")
+
+converter = pipeline("translation",
+                      "facebook/seamless-m4t-v2-large",
+                      torch_dtype=torch.float32,
+                      device="cpu")
+
+
+asr_text = translator("https://huggingface.co/datasets/reach-vb/random-audios/resolve/main/ted_60.wav",
+           chunk_length_s=30,
+           generate_kwargs={"tgt_lang": "eng"})
+
+print(asr_text)
+
+# print(translator("https://huggingface.co/datasets/reach-vb/random-audios/resolve/main/ted_60.wav",
+#            chunk_length_s=30,
+#            generate_kwargs={"tgt_lang": "fra"}))
+
+# print(translator("https://huggingface.co/datasets/reach-vb/random-audios/resolve/main/ted_60.wav",
+#            chunk_length_s=30,
+#            generate_kwargs={"tgt_lang": "ita"}))
+
+# print(translator("https://huggingface.co/datasets/reach-vb/random-audios/resolve/main/ted_60.wav",
+#            chunk_length_s=30,
+#            generate_kwargs={"tgt_lang": "spa"}))
+
+
+print(converter(asr_text['text'],
+           src_lang="eng", tgt_lang="spa")
+    )